ORCID Profile
0000-0001-5715-7154
Current Organisation
University of Technology Sydney
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Artificial Intelligence and Image Processing | Pattern Recognition and Data Mining | Information Storage, Retrieval And Management | Information Systems Development Methodologies | Pattern Recognition | Other Artificial Intelligence | Business Information Systems (Incl. Data Processing) | Library and Information Studies | Business and Management | Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence) | Database Management | Simulation And Modelling | Information Systems | Computer Hardware not elsewhere classified | Database Management | Natural Language Processing | Simulation and Modelling | Computer Hardware | Decision Support And Group Support Systems |
ANZSRC Socio-Economic Objective (SEO)
Information processing services | Application tools and system utilities | Information Processing Services (incl. Data Entry and Capture) | Application Tools and System Utilities | Application Software Packages (excl. Computer Games) | Application packages | Computer software and services not elsewhere classified | Expanding Knowledge in the Information and Computing Sciences | Property, Business Support Services and Trade not elsewhere classified | Changing work patterns | Wholesale and Retail Trade | Expanding Knowledge in Technology | Community services not elsewhere classified | Electronic Information Storage and Retrieval Services | Mobile Data Networks and Services | Information and Communication Services not elsewhere classified | Commercial security services | Health Policy Evaluation
Related Links
Publications
A general model-based diagnosis
Publisher: Univ. Zagreb
Date: 2003
Mining Probabilistic Representative Frequent Patterns From Uncertain Data
Publisher: Society for Industrial and Applied Mathematics
Date: 02-05-2013
A logic framework with algebraic extension
Publisher: Univ. Zagreb
Date: 2003
Binarized attributed network embedding
Publisher: IEEE
Date: 11-2018
A unified algebraic structure for uncertain reasonings
Publisher: Springer Berlin Heidelberg
Date: 1996
Introduction to the Special Issue on Learning in Intelligent Algorithms and Systems Design
Publisher: Fuji Technology Press Ltd.
Date: 20-12-1999
Compound Rank-k Projections for Bilinear Analysis
Publisher: arXiv
Date: 2014
An agent-based curve-digitizing system for well-logging data management
Publisher: IEEE Comput. Soc
Date: 2003
Online Learning from Trapezoidal Data Streams
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Learning Colours from Textures by Sparse Manifold Embedding
Publisher: Springer Berlin Heidelberg
Date: 2011
DDDM2007
Publisher: Association for Computing Machinery (ACM)
Date: 12-2007
Abstract: Real-world data mining generally must consider and involve domain and business oriented factors such as human knowledge, constraints and business expectations. This encourages the development of a domain driven methodology to strengthen data-centered pattern mining. This report presents a review of the ACM SIGKDD Workshop on Domain Driven Data Mining (DDDM2007), held in conjunction with the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD07), which was held in San Jose, USA on 12 August, 2007. The aims and objectives of this workshop were to provide a premier forum for sharing innovative findings, knowledge, insights, experiences and lessons in tackling challenges met in domain driven, actionable knowledge discovery in the real world.
A Comprehensive Survey on Graph Neural Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However, the explicit graph structure (relation) does not necessarily reflect the true dependency and genuine relation may be missing due to the incomplete connections in the data. Furthermore, existing methods are ineffective to capture the temporal trends as the RNNs or CNNs employed in these methods cannot capture long-range temporal sequences. To overcome these limitations, we propose in this paper a novel graph neural network architecture, {Graph WaveNet}, for spatial-temporal graph modeling. By developing a novel adaptive dependency matrix and learn it through node embedding, our model can precisely capture the hidden spatial dependency in the data. With a stacked dilated 1D convolution component whose receptive field grows exponentially as the number of layers increases, Graph WaveNet is able to handle very long sequences. These two components are integrated seamlessly in a unified framework and the whole framework is learned in an end-to-end manner. Experimental results on two public traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superior performance of our algorithm.
Engineering open complex agent systems: A case study
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
Preface
Publisher: Springer Science and Business Media LLC
Date: 05-2013
Detecting inconsistency in biological molecular databases using ontologies
Publisher: Springer Science and Business Media LLC
Date: 11-07-2007
AI 2005: Advances in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11589990
Identifying Interesting Customers through Web Log Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2005
DOI: 10.1109/MIS.2005.47
Identifying frequent terms in text databases by association semantics
Publisher: IEEE Comput. Soc
Date: 2003
K-Ary Tree Hashing for Fast Graph Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
Cost Sensitive Classification in Data Mining
Publisher: Springer Berlin Heidelberg
Date: 2010
Dynamic Affinity Graph Construction for Spectral Clustering Using Multiple Features.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Reading Materials
Publisher: Springer US
Date: 12-12-2009
Mining Actionable Knowledge on Social Security Data
Publisher: Springer US
Date: 12-12-2009
Open Issues and Prospects
Publisher: Springer US
Date: 12-12-2009
RCP Mining: Towards the Summarization of Spatial Co-location Patterns
Publisher: Springer International Publishing
Date: 2015
Cost-sensitive Hybrid Neural Networks for Heterogeneous and Imbalanced Data
Publisher: IEEE
Date: 07-2018
WI 2008 Welcome Message from Conference Chairs and Program Chair
Publisher: IEEE
Date: 2008
DOI: 10.1109/WIIAT.2008.4
Federated Learning for Open Banking
Publisher: Springer International Publishing
Date: 2020
Ubiquitous Intelligence in Agent Mining
Publisher: Springer Berlin Heidelberg
Date: 2009
The Evolution of KDD: Towards Domain-Driven Data Mining
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2007
DOI: 10.1142/S0218001407005612
Abstract: Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information, in which domain intelligence may not be necessary in targeting the demonstration of an algorithm. Often knowledge discovered is not generally interesting to business needs. Comparably, real-world applications rely on knowledge for taking effective actions. In retrospect of the evolution of KDD, this paper briefly introduces domain-driven data mining to complement traditional KDD. Domain intelligence is highlighted towards actionable knowledge discovery, which involves aspects such as domain knowledge, people, environment and evaluation. We illustrate it through mining activity patterns in social security data.
A Review for Weighted MinHash Algorithms (Extended abstract)
Publisher: IEEE
Date: 04-2023
Horizontal cooperation under uncertainties in distributed expert systems
Publisher: IEEE Comput. Soc
Date: 1999
Dual instance and attribute weighting for Naive Bayes classification
Publisher: IEEE
Date: 07-2014
Learning colours from textures by sparse manifold embedding
Publisher: Elsevier BV
Date: 06-2013
Rating Knowledge Sharing in Cross-Domain Collaborative Filtering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Cooperation under uncertainty in distributed expert systems
Publisher: Elsevier BV
Date: 07-1992
Multi-instance Multi-graph Dual Embedding Learning
Publisher: IEEE
Date: 12-2013
Introduction
Publisher: Springer Berlin Heidelberg
Date: 2008
Agent-Based Hybrid Intelligent Systems
Publisher: Springer Berlin Heidelberg
Date: 2004
DOI: 10.1007/B95170
A Class of Isomorphic Transformations for Integrating EMYCIN-Style and PROSPECTOR-Style Systems into a Rule-Based Multi-Agent System
Publisher: Springer Berlin Heidelberg
Date: 1999
Detecting Collusion Attacks in Security Protocols
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11610113_27
Agent-Based Distributed Data Mining: A Survey
Publisher: Springer US
Date: 2009
Reinforcement Learning With Multiple Relational Attention for Solving Vehicle Routing Problems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Agent services-oriented architectural design of open complex agent systems
Publisher: IEEE
Date: 2005
DOI: 10.1109/IAT.2005.28
CFOND: Consensus Factorization for Co-Clustering Networked Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
A Multiple System Performance Monitoring Model for Web Services
Publisher: Springer Berlin Heidelberg
Date: 2010
Defragging Subgraph Features for Graph Classification
Publisher: ACM
Date: 17-10-2015
A Novel Prototype Reduction Method for the K-Nearest Neighbor Algorithm with K >= 1
Publisher: Springer Berlin Heidelberg
Date: 2010
Online diffusion source detection in social networks
Publisher: IEEE
Date: 07-2015
Mining Both Positive and Negative Impact-Oriented Sequential Rules from Transactional Data
Publisher: Springer Berlin Heidelberg
Date: 2009
An Empirical Study of Bagging Predictors for Imbalanced Data with Different Levels of Class Distribution
Publisher: Springer Berlin Heidelberg
Date: 2011
Intelligent Agent
Publisher: Wiley
Date: 14-12-2007
DOI: 10.1002/9780470050118.ECSE194
Abstract: This article provides an insight for general readers on what intelligent agent is in the context of computer science and engineering. It describes the major developments that have led up to the current great interest in intelligent agents and then presents a brief discussion of the principal scientific and engineering issues in the field, which include the architecture of intelligent agents, communication languages for intelligent agents, and the methodologies and development tools for building intelligent agents. The typical applications and technological challenges for research and development over the next decade are also summarized.
Mining Dependent Frequent Serial Episodes from Uncertain Sequence Data
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/ICDM.2013.35
Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment: a case study
Publisher: Elsevier BV
Date: 06-2001
Clustering High-Dimensional Data with Low-Order Neighbors
Publisher: IEEE
Date: 2004
An improved model-based method to test circuit faults
Publisher: Elsevier BV
Date: 09-2005
Welcome Message from the Conference Chairs
Publisher: IEEE
Date: 12-2010
DOI: 10.1109/ICDMW.2010.5
Estimating confidence intervals for structural differences between contrast groups with missing data
Publisher: Elsevier BV
Date: 04-2009
Salient Subsequence Learning for Time Series Clustering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
Multi-graph-view subgraph mining for graph classification
Publisher: Springer Science and Business Media LLC
Date: 21-09-2016
Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams
Publisher: IEEE
Date: 12-2012
ARMGA: IDENTIFYING INTERESTING ASSOCIATION RULES WITH GENETIC ALGORITHMS
Publisher: Informa UK Limited
Date: 08-2005
Dual influence embedded social recommendation
Publisher: Springer Science and Business Media LLC
Date: 24-08-2018
Combined Mining: Discovering Informative Knowledge in Complex Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2011
Multiple Structure-View Learning for Graph Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Modeling Conserved Structure Patterns for Functional Noncoding RNA
Publisher: Springer International Publishing
Date: 2014
Theoretical Evaluation of Ring-Based Architectural Model for Middle Agents in Agent-Based System
Publisher: Springer Berlin Heidelberg
Date: 2003
An efficient strategy for mining exceptions in multi-databases
Publisher: Elsevier BV
Date: 09-2004
Interval Based Similarity for Function Classification of RNA Pseudoknots
Publisher: Springer International Publishing
Date: 2014
Identifying bridging rules between conceptual clusters
Publisher: ACM
Date: 20-08-2006
Mining Protein Kinase Regulation Using Graphical Models
Publisher: Springer International Publishing
Date: 2014
Mining Inhibition Pathways for Protein Kinases on Skeletal Muscle
Publisher: Springer International Publishing
Date: 2014
Isomorphic transformations of uncertainties for incorporating EMYCIN-style and PROSPECTOR-style systems into a distributed expert system
Publisher: Springer Science and Business Media LLC
Date: 07-1999
DOI: 10.1007/BF02948741
Build Emotion Lexicon from Microblogs by Combining Effects of Seed Words and Emoticons in a Heterogeneous Graph
Publisher: ACM Press
Date: 2015
Discovery of Structural and Functional Features Bind to RNA Pseudoknots
Publisher: Springer International Publishing
Date: 2014
Detecting Inconsistency in Biological Molecular Databases Using Ontologies
Publisher: Springer International Publishing
Date: 2014
Convex Sparse PCA for Unsupervised Feature Learning
Publisher: Association for Computing Machinery (ACM)
Date: 20-07-2016
DOI: 10.1145/2910585
Abstract: Principal component analysis (PCA) has been widely applied to dimensionality reduction and data pre-processing for different applications in engineering, biology, social science, and the like. Classical PCA and its variants seek for linear projections of the original variables to obtain the low-dimensional feature representations with maximal variance. One limitation is that it is difficult to interpret the results of PCA. Besides, the classical PCA is vulnerable to certain noisy data. In this paper, we propose a Convex Sparse Principal Component Analysis (CSPCA) algorithm and apply it to feature learning. First, we show that PCA can be formulated as a low-rank regression optimization problem. Based on the discussion, the l 2, 1 -normminimization is incorporated into the objective function to make the regression coefficients sparse, thereby robust to the outliers. Also, based on the sparse model used in CSPCA, an optimal weight is assigned to each of the original feature, which in turn provides the output with good interpretability. With the output of our CSPCA, we can effectively analyze the importance of each feature under the PCA criteria. Our new objective function is convex, and we propose an iterative algorithm to optimize it. We apply the CSPCA algorithm to feature selection and conduct extensive experiments on seven benchmark datasets. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art unsupervised feature selection algorithms.
Exploration of Positive Frequent Patterns for AMP-Activated Protein Kinase Regulation
Publisher: Springer International Publishing
Date: 2014
Introduction
Publisher: Springer International Publishing
Date: 2014
Data Resources and Applications
Publisher: Springer International Publishing
Date: 2014
TrGraph: Cross-network transfer learning via common signature subgraphs
Publisher: IEEE
Date: 05-2016
Computing the minimum-support for mining frequent patterns
Publisher: Springer Science and Business Media LLC
Date: 06-04-2007
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection
Publisher: IEEE
Date: 18-07-2022
Compound Rank-k Projections for Bilinear Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Detecting Turning Points of Trading Price and Return Volatility for Market
Publisher: IEEE
Date: 11-2007
Dynamic Concept Composition for Zero-Example Event Detection
Publisher: arXiv
Date: 2016
Mining Frequent Itemsets for Protein Kinase Regulation
Publisher: Springer Berlin Heidelberg
Date: 2006
Data mining for business applications
Publisher: Springer US
Date: 2009
CLAP: Collaborative pattern mining for distributed information systems
Publisher: Elsevier BV
Date: 12-2011
An agent-based framework for petroleum information services from distributed heterogeneous data resources
Publisher: IEEE Comput. Soc
Date: 2002
Universal programmable devices for unambiguous discrimination
Publisher: American Physical Society (APS)
Date: 10-10-2006
Integration of Business Intelligence Based on Three-Level Ontology Services
Publisher: IEEE
Date: 2004
A neural network diagnosis model without disorder independence assumption
Publisher: Springer Berlin Heidelberg
Date: 1998
DOI: 10.1007/BFB0095282
Application of MAS in implementing rational IP routers on the priced internet
Publisher: Springer Berlin Heidelberg
Date: 1997
DOI: 10.1007/BFB0030089
Fuzzy genetic algorithms for pairs mining
Publisher: Springer Berlin Heidelberg
Date: 2006
FedProto: Federated Prototype Learning across Heterogeneous Clients
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 28-06-2022
Abstract: Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space. For ex le, clients may differ in terms of data distribution, network latency, input/output space, and/or model architecture, which can easily lead to the misalignment of their local gradients. To improve the tolerance to heterogeneity, we propose a novel federated prototype learning (FedProto) framework in which the clients and server communicate the abstract class prototypes instead of the gradients. FedProto aggregates the local prototypes collected from different clients, and then sends the global prototypes back to all clients to regularize the training of local models. The training on each client aims to minimize the classification error on the local data while keeping the resulting local prototypes sufficiently close to the corresponding global ones. Moreover, we provide a theoretical analysis to the convergence rate of FedProto under non-convex objectives. In experiments, we propose a benchmark setting tailored for heterogeneous FL, with FedProto outperforming several recent FL approaches on multiple datasets.
Agents and data mining: Mutual enhancement by integration
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11492870_5
Interval-Based Similarity for Classifying Conserved RNA Secondary Structures
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
DOI: 10.1109/MIS.2015.2
Boosting for graph classification with universum
Publisher: Springer Science and Business Media LLC
Date: 19-03-2017
Cortical Gyrification And Sulcal Spans In Early Stage Alzheimer'S Disease
Publisher: Public Library of Science (PLoS)
Date: 21-02-2012
Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together
Publisher: Association for Computational Linguistics
Date: 2019
DOI: 10.18653/V1/N19-1127
Heterogeneous transformation of uncertainties of propositions among inexact reasoning models
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-1994
DOI: 10.1109/69.334865
Deconstruction waste management through 3D reconstruction and BIM: a case study
Publisher: Springer Science and Business Media LLC
Date: 14-07-2017
DOI: 10.1186/S40327-017-0050-5
Abstract: The construction industry is responsible for 50% of the solid waste generated worldwide. Governments around the world formulate legislation and regulations concerning recycling and re-using building materials, aiming to reduce waste and environmental impact. Researchers have also been developing strategies and models of waste management for construction and demolition of buildings. The application of Building Information Modeling (BIM) is an ex le of this. BIM is emergent technology commonly used to maximize the efficiency of design, construction and maintenance throughout the entire lifecycle. The uses of BIM on deconstruction or demolition are not common especially the fixtures and fittings of buildings are not considered in BIM models. The development of BIM is based on two-dimensional drawings or sketches, which may not be accurately converted to 3D BIM models. In addition, previous researches mainly focused on construction waste management. There are few studies about the deconstruction waste management focusing on demolition. To fill this gap, this paper aims to develop a framework using a reconstructed 3D model with BIM, for the purpose of improving BIM accuracy and thus developing a deconstruction waste management system to improve demolition efficiency, effective recycling and cost savings. In particular, the developed as-built BIM will be used to identify and measure recyclable materials, as well as to develop a plan for the recycling process.
F-TRADE 3.0: An Agent-Based Integrated Framework for Data Mining Experiments
Publisher: IEEE
Date: 12-2008
Methodologies Of Solution Synthesis In Distributed Expert Systems
Publisher: Springer Berlin Heidelberg
Date: 1997
DOI: 10.1007/BFB0030087
Approaches to incorporating soft computing technologies into software agents
Publisher: IEEE
Date: 1999
Positive and Unlabeled Multi-Graph Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2017
Combining structured node content and topology information for networked graph clustering
Publisher: Association for Computing Machinery (ACM)
Date: 21-03-2017
DOI: 10.1145/2996197
Abstract: Graphs are popularly used to represent objects with shared dependency relationships. To date, all existing graph clustering algorithms consider each node as a single attribute or a set of independent attributes, without realizing that content inside each node may also have complex structures. In this article, we formulate a new networked graph clustering task where a network contains a set of inter-connected (or networked) super-nodes, each of which is a single-attribute graph. The new super-node representation is applicable to many real-world applications, such as a citation network where each node denotes a paper whose content can be described as a graph, and citation relationships between papers form a networked graph (i.e., a super-graph). Networked graph clustering aims to find similar node groups, each of which contains nodes with similar content and structure information. The main challenge is to properly calculate the similarity between super-nodes for clustering. To solve the problem, we propose to characterize node similarity by integrating structure and content information of each super-node. To measure node content similarity, we use cosine distance by considering overlapped attributes between two super-nodes. To measure structure similarity, we propose an Attributed Random Walk Kernel (ARWK) to calculate the similarity between super-nodes. Detailed node content analysis is also included to build relationships between super-nodes with shared internal structure information, so the structure similarity can be calculated in a precise way. By integrating the structure similarity and content similarity as one matrix, the spectral clustering is used to achieve networked graph clustering. Our method enjoys sound theoretical properties, including bounded similarities and better structure similarity assessment than traditional graph clustering methods. Experiments on real-world applications demonstrate that our method significantly outperforms baseline approaches.
LSLNCF: A Hybrid Uncertain Reasoning Model Based on Probability
Publisher: World Scientific Pub Co Pte Lt
Date: 08-1998
DOI: 10.1142/S021848859800032X
Abstract: The "take-them-in-or-leave-them-out" of prior probabilities is a key problem in uncertain reasonings. The EMYCIN uncertain reasoning model is inconsistent with probability theory, due to 'leaving them out', while the PROSPECTOR uncertain reasoning model is substantially consistent with probability theory, due to 'taking them in'. This presents a difficult task for human experts when supplying prior probabilities. In this paper, in order to overcome the difficulty, we propose a hybrid uncertain reasoning model, by combing rule strengths in the PROSPECTOR model with rule strengths in the EMYCIN model. Moreover, different forms of rule strength will enable human experts to supply values for the rule strengths more flexibly in a knowledge base. Finally, an ex le is given to illustrate the power of our methodology.
The effect of varying levels of class distribution on bagging for different algorithms: An empirical study
Publisher: Springer Science and Business Media LLC
Date: 04-11-2012
Any-Cost Discovery: Learning Optimal Classification Rules
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11589990_15
POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases
Publisher: Elsevier BV
Date: 03-2009
A Review for Weighted MinHash Algorithms
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
User Profile Preserving Social Network Embedding
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2017
Abstract: This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
Locally Densest Subgraph Discovery
Publisher: ACM
Date: 10-08-2015
An Agent-Based Soft Computing Society
Publisher: Springer Berlin Heidelberg
Date: 2001
Dual Personalization on Federated Recommendation
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2023
Abstract: Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of heavyweight models at the server and hinders the deployment of on-device intelligent models to end-users. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight model on a server. Moreover, we propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items. The overall learning process is formulated into a unified federated optimization framework. Specifically, unlike previous methods that share exactly the same item embeddings across users in a federated system, dual personalization allows mild finetuning of item embeddings for each user to generate user-specific views for item representations which can be integrated into existing federated recommendation methods to gain improvements immediately. Experiments on multiple benchmark datasets have demonstrated the effectiveness of PFedRec and the dual personalization mechanism. Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings. The code is available.
Attributed network embedding via subspace discovery
Publisher: Springer Science and Business Media LLC
Date: 26-08-2019
Multi-Agent Systems Methodologies and Applications
Publisher: Springer Berlin Heidelberg
Date: 1997
DOI: 10.1007/BFB0030077
Mining for combined association rules on multiple datasets
Publisher: ACM
Date: 12-08-2007
Database clustering for mining multi-databases
Publisher: IEEE
Date: 2002
Incremental Subgraph Feature Selection for Graph Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
A Decremental Algorithm for Maintaining Frequent Itemsets in Dynamic Databases
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11546849_30
Robust Clustered Federated Learning with Bootstrap Median-of-Means
Publisher: Springer Nature Switzerland
Date: 2023
Data Science
Publisher: Springer International Publishing
Date: 2015
Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2017
Abstract: Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA). Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of reviews, user preferences and product characteristics by jointly optimizing two components, a user-specific LFM and a product-specific LFM, each of which decomposes text into a specific low-dimension representation. Furthermore, we address the cold-start issue by developing a novel Pairwise Rating Comparison strategy (PRC), which utilizes the difference between ratings on common user roduct as supplementary information to calibrate parameter estimation. Experiments conducted on IMDB and Yelp datasets validate the advantage of our approach over state-of-the-art baseline methods.
Flexible Frameworks For Actionable Knowledge Discovery
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2010
MINING DEPENDENT PATTERNS IN PROBABILISTIC DATABASES
Publisher: Informa UK Limited
Date: 06-2004
Efficient Attributed Network Embedding via Recursive Randomized Hashing
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Attributed network embedding aims to learn a low-dimensional representation for each node of a network, considering both attributes and structure information of the node. However, the learning based methods usually involve substantial cost in time, which makes them impractical without the help of a powerful workhorse. In this paper, we propose a simple yet effective algorithm, named NetHash, to solve this problem only with moderate computing capacity. NetHash employs the randomized hashing technique to encode shallow trees, each of which is rooted at a node of the network. The main idea is to efficiently encode both attributes and structure information of each node by recursively sketching the corresponding rooted tree from bottom (i.e., the predefined highest-order neighboring nodes) to top (i.e., the root node), and particularly, to preserve as much information closer to the root node as possible. Our extensive experimental results show that the proposed algorithm, which does not need learning, runs significantly faster than the state-of-the-art learning-based network embedding methods while achieving competitive or even better performance in accuracy.
Multi-sphere Support Vector Data for Outliers Detection on Multi-distribution Data
Publisher: IEEE
Date: 12-2009
Adapting K-Means Algorithm for Discovering Clusters in Subspaces
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11610113_6
Agent-Mining Interaction: An Emerging Area
Publisher: Springer Berlin Heidelberg
Date: 2007
SocialAnalysis: A Real-Time Query and Mining System from Social Media Data Streams
Publisher: Springer International Publishing
Date: 2015
Developing actionable trading strategies for trading agents
Publisher: IEEE
Date: 11-2007
DOI: 10.1109/IAT.2007.16
Rare class association rule mining with multiple imbalanced attributes
Publisher: IGI Global
Date: 2010
DOI: 10.4018/978-1-60566-754-6.CH005
Abstract: In this chapter, the authors propose a novel framework for rare class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This algorithm is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through a standard algorithm while the rules with imbalanced attributes are mined based on newly defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied in the social security field. Although some attributes are severely imbalanced, rules with a minority of imbalanced attributes have been mined efficiently.
Life in the 'family friendly' 21st Century: Finding the balance between work and family
Publisher: Informa UK Limited
Date: 2006
Agent Services-Driven-plug-and-play in F-Trade
Publisher: Springer Berlin Heidelberg
Date: 2004
Multi-graph-view Learning for Graph Classification
Publisher: IEEE
Date: 12-2014
DOI: 10.1109/ICDM.2014.97
Towards anomalous diffusion sources detection in a large network
Publisher: Association for Computing Machinery (ACM)
Date: 18-01-2016
DOI: 10.1145/2806889
Abstract: Witnessing the wide spread of malicious information in large networks, we develop an efficient method to detect anomalous diffusion sources and thus protect networks from security and privacy attacks. To date, most existing work on diffusion sources detection are based on the assumption that network snapshots that reflect information diffusion can be obtained continuously. However, obtaining snapshots of an entire network needs to deploy detectors on all network nodes and thus is very expensive. Alternatively, in this article, we study the diffusion sources locating problem by learning from information diffusion data collected from only a small subset of network nodes. Specifically, we present a new regression learning model that can detect anomalous diffusion sources by jointly solving five challenges, that is, unknown number of source nodes, few activated detectors, unknown initial propagation time, uncertain propagation path and uncertain propagation time delay. We theoretically analyze the strength of the model and derive performance bounds. We empirically test and compare the model using both synthetic and real-world networks to demonstrate its performance.
Task Sensitive Feature Exploration and Learning for Multitask Graph Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2017
SINE: Scalable Incomplete Network Embedding
Publisher: IEEE
Date: 11-2018
TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Data preparation for data mining
Publisher: Informa UK Limited
Date: 05-2003
DOI: 10.1080/713827180
Potential Cases, Methodologies, And Strategies Of Synthesis Of Solutions In Distributed Expert Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/69.774105
Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Margin-Based Ensemble Classifier For Protein Fold Recognition
Publisher: Elsevier BV
Date: 09-2011
ENDL: A Logical Framework for Verifying Secure Transaction Protocols
Publisher: Springer Science and Business Media LLC
Date: 2005
Developing Actionable Trading Strategies for Trading Agents
Publisher: IEEE
Date: 11-2007
DOI: 10.1109/IAT.2007.47
Cost-sensitive classification with inadequate labeled data
Publisher: Elsevier BV
Date: 07-2012
Agent-Based Subspace Clustering
Publisher: Springer Berlin Heidelberg
Date: 2011
Consistent Weighted Sampling Made More Practical
Publisher: International World Wide Web Conferences Steering Committee
Date: 03-04-2017
IAT 2008 Welcome Message from Conference Chairs and Program Chair
Publisher: IEEE
Date: 2008
An agent-based hybrid framework for database mining
Publisher: Informa UK Limited
Date: 05-2003
DOI: 10.1080/713827179
Cooperative strategy for web data mining and cleaning
Publisher: Informa UK Limited
Date: 05-2003
DOI: 10.1080/713827173
e-NSP: efficient negative sequential pattern mining based on identified positive patterns without database rescanning
Publisher: ACM
Date: 24-10-2011
SODE: Self-Adaptive One-Dependence Estimators for classification
Publisher: Elsevier BV
Date: 03-2016
Mask- and Contrast-Enhanced Spatio-Temporal Learning for Urban Flow Prediction
Publisher: ACM
Date: 21-10-2023
TOIS reviewers January 2006 through May 2007
Publisher: Association for Computing Machinery (ACM)
Date: 10-2007
A Logical Framework for Knowledge Sharing in Multi-agent Systems
Publisher: Springer Berlin Heidelberg
Date: 2001
Scalable big graph processing in MapReduce
Publisher: ACM
Date: 18-06-2014
Collective Classification via Discriminative Matrix Factorization on Sparsely Labeled Networks
Publisher: ACM
Date: 24-10-2016
Towards Mining Trapezoidal Data Streams
Publisher: IEEE
Date: 11-2015
DOI: 10.1109/ICDM.2015.42
An Efficient Implementation of the Backtesting of Trading Strategies
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11576235_17
Multi-Graph Learning with Positive and Unlabeled Bags
Publisher: Society for Industrial and Applied Mathematics
Date: 28-04-2014
Global and local influence-based social recommendation
Publisher: ACM
Date: 24-10-2016
Build Emotion Lexicon from the Mood of Crowd via Topic-Assisted Joint Non-negative Matrix Factorization
Publisher: ACM
Date: 07-07-2016
Self-adaptive attribute weighting for Naive Bayes classification
Publisher: Elsevier BV
Date: 02-2015
Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Efficient Mining of Event-Oriented Negative Sequential Rules
Publisher: IEEE
Date: 12-2008
Multi-Instance Learning from Positive and Unlabeled Bags
Publisher: Springer International Publishing
Date: 2014
Advances in processing, mining, and learning complex data: From foundations to real-world applications
Publisher: Hindawi Limited
Date: 19-07-2018
DOI: 10.1155/2018/7861860
Propagating temporal relations of intervals by matrix
Publisher: Informa UK Limited
Date: 2002
Efficient mining of both positive and negative association rules
Publisher: Association for Computing Machinery (ACM)
Date: 07-2004
Abstract: This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A ⇒ ¬ B , ¬ A ⇒ B , and ¬ A ⇒ ¬ B , which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its effectiveness and efficiency.
Activity mining: From activities to actions
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2008
DOI: 10.1142/S0219622008002934
Abstract: Activity data accumulated in real life, such as terrorist activities and governmental customer contacts, present special structural and semantic complexities. Activity data may lead to or be associated with significant business impacts, and result in important actions and decision making leading to business advantage. For instance, a series of terrorist activities may trigger a disaster to society, and large amounts of fraudulent activities in social security programs may result in huge government customer debt. Uncovering these activities or activity sequences can greatly evidence and/or enhance corresponding actions in business decisions. However, mining such data challenges the existing KDD research in aspects such as unbalanced data distribution and impact-targeted pattern mining. This paper investigates the characteristics and challenges of activity data, and the methodologies and tasks of activity mining based on case-study experience in the area of social security. Activity mining aims to discover high impact activity patterns in huge volumes of unbalanced activity transactions. Activity patterns identified can be used to prevent disastrous events or improve business decision making and processes. We illustrate the above issues and prospects in mining governmental customer contacts data to recover customer debt.
Conclusion and FutureWorks
Publisher: Springer International Publishing
Date: 2014
Bioinformatics-Based Drug Discovery for Protein Kinases
Publisher: Springer International Publishing
Date: 2014
ON DATA STRUCTURES FOR ASSOCIATION RULE DISCOVERY
Publisher: Informa UK Limited
Date: 08-02-2007
Discovering Conserved and Diverged Patterns of MiRNA Families
Publisher: Springer International Publishing
Date: 2014
Mining Featured Patterns of MiRNA Interaction Based on Sequence and Structure Similarity
Publisher: Springer International Publishing
Date: 2014
An Axiom Foundation for Uncertain Reasonings in Rule-Based Expert Systems: NT-Algebra
Publisher: Springer Science and Business Media LLC
Date: 11-1999
DOI: 10.1007/BF03325108
Probabilistic Exposure Fusion
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2012
Cross-aligned and Gumbel-refactored Autoencoders for Multi-view Anomaly Detection
Publisher: IEEE
Date: 11-2021
Multi-instance learning with discriminative bag mapping
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Post-mining: maintenance of association rules by weighting
Publisher: Elsevier BV
Date: 10-2003
Testing Adaptive Local Hyperplane for multi-class classification by double cross-validation
Publisher: IEEE
Date: 07-2010
Domain Driven Data Mining
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-59904-960-1.CH009
Abstract: Input selection is a crucial step for nonlinear regression modeling problem, which contributes to build an interpretable model with less computation. Most of the available methods are model-based, and few of them are model-free. Model-based methods often make use of prediction error or sensitivity analysis for input selection and Model-free methods exploit consistency. In this paper, we show the underlying relationship between sensitivity analysis and consistency analysis for input selection, and then derive an efficient model-free method from our common sense, and then formulate this common sense by fuzzy logic, thus it can be called Fuzzy Consistency Analysis (FCA). In contrast to available methods, FCA has the following desirable properties: 1) it is a model-free method so that it will not be biased on a specific model, exploiting “what the data say” rather than “what the model say”, which is the essential point of data mining – input selection should not be biased on a specific model. 2) it is implemented as efficiently as classical model-free methods, but more flexible than them. 3) it can be directly applied to a data set with mix continuous and discrete inputs without doing rotation. Four benchmark problems study indicates that the proposed method works effectively for nonlinear problems. With the input selection procedure, the underlying reasons which effect the prediction are work out, which helps to gain an insight into a specific problem and servers the purpose of data mining very well.
Domain Driven Data Mining
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-59904-960-1.CH008
Abstract: Quantitative intelligence based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications. For instance, the usual demonstration of specific algorithms cannot support business users to take actions to their advantage and needs. We think this is due to Quantitative Intelligence focused data-driven philosophy. It either views data mining as an autonomous data-driven, trial-and-error process, or only analyzes business issues in an isolated, case-by-case manner. Based on experience and lessons learnt from real-world data mining and complex systems, this article proposes a practical data mining methodology referred to as Domain-Driven Data Mining. On top of quantitative intelligence and hidden knowledge in data, domain-driven data mining aims to meta-synthesize quantitative intelligence and qualitative intelligence in mining complex applications in which human is in the loop. It targets actionable knowledge discovery in constrained environment for satisfying user preference. Domain-driven methodology consists of key components including understanding constrained environment, business-technical questionnaire, representing and involving domain knowledge, human-mining cooperation and interaction, constructing next-generation mining infrastructure, in-depth pattern mining and postprocessing, business interestingness and actionability enhancement, and loop-closed human-cooperated iterative refinement. Domain-driven data mining complements the data-driven methodology, the metasynthesis of qualitative intelligence and quantitative intelligence has potential to discover knowledge from complex systems, and enhance knowledge actionability for practical use by industry and business.
Cost-Sensitive Decision Trees with Multiple Cost Scales
Publisher: Springer Berlin Heidelberg
Date: 2004
A Recent-Biased Dimension Reduction Technique for Time Series Data
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11430919_87
Secure Agent-Mediated Auctionlike Negotiation Protocol for Internet Retail Commerce
Publisher: Springer Berlin Heidelberg
Date: 1999
Adversarially Regularized Graph Autoencoder for Graph Embedding
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in real-world graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding, two variants of adversarial approaches, adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction, graph clustering, and graph visualization tasks.
SNOC: Streaming Network Node Classification
Publisher: IEEE
Date: 12-2014
DOI: 10.1109/ICDM.2014.55
Discovering causality in large databases
Publisher: Informa UK Limited
Date: 05-2002
The design and implementation of a knowledge-based communication system in a framework for distributed expert systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-1995
DOI: 10.1109/26.387428
An efficient and simple under-sampling technique for imbalanced time series classification
Publisher: ACM
Date: 29-10-2012
Secure Transaction Protocol Analysis
Publisher: Springer Berlin Heidelberg
Date: 2008
A HYBRID MODEL FOR SHARING INFORMATION BETWEEN FUZZY, UNCERTAIN AND DEFAULT REASONING MODELS IN MULTI-AGENT SYSTEMS
Publisher: World Scientific Pub Co Pte Lt
Date: 08-2002
DOI: 10.1142/S0218488502001557
Abstract: This paper develops a hybrid model which provides a unified framework for the following four kinds of reasoning: 1) Zadeh's fuzzy approximate reasoning 2) truth-qualification uncertain reasoning with respect to fuzzy propositions 3) fuzzy default reasoning (proposed, in this paper, as an extension of Reiter's default reasoning) and 4) truth-qualification uncertain default reasoning associated with fuzzy statements (developed in this paper to enrich fuzzy default reasoning with uncertain information). Our hybrid model has the following characteristics: 1) basic uncertainty is estimated in terms of words or phrases in natural language and basic propositions are fuzzy 2) uncertainty, linguistically expressed, can be handled in default reasoning and 3) the four kinds of reasoning models mentioned above and their combination models will be the special cases of our hybrid model. Moreover, our model allows the reasoning to be performed in the case in which the information is fuzzy, uncertain and partial. More importantly, the problems of sharing the information among heterogeneous fuzzy, uncertain and default reasoning models can be solved efficiently by using our model. Given this, our framework can be used as a basis for information sharing and exchange in knowledge-based multi-agent systems for practical applications such as automated group negotiations. Actually, to build such a foundation is the motivation of this paper.
An Efficient Method for Modeling Nonoccurring Behaviors by Negative Sequential Patterns With Loose Constraints.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Dynamic Test-Sensitive Decision Trees with Multiple Cost Scales
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11539506_52
An information filtering model on the Web and its application in JobAgent
Publisher: Elsevier BV
Date: 10-2000
IMC: A Method for Interval Calculus in Matrix
Publisher: Springer Science and Business Media LLC
Date: 05-1999
DOI: 10.1007/BF03325100
Preface
Publisher: Walter de Gruyter GmbH
Date: 2012
Abstract: In 2005 at the centennial anniversary of Fudan University, Shanghai, China, a new conference venue began [1]. This venue, the International Conference on Novel Materials and Synthesis (NMS) together with the International Symposium on Fine Chemistry and Functional Polymers (FCFP), is targeted to provide high-level academic exchange for both local and international chemists, materialists, physicists, engineers, and technologists in the fields of materials and synthesis. The year 2011 was the International Year of Chemistry, and it is well known that chemistry is an essential creative science for the sustainable development of humankind. As a result, the joint NMSVII/ FCFP-XXI event (www.nms-iupac.org), held in Shanghai, China, 16-21 October 2011, was more important than ever. The Conference received much support from IUPAC, The National Natural Science Foundation of China, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, the Science and Technology Commission of the Shanghai Municipality, and the National Basic Research Program of China (2007CB209700), and was carried out under the auspices of IUPAC. The Conference was attended by 430 participants from 40 countries and areas. The scientific program comprised 10 plenary lectures, 56 keynote lectures, 206 invited lectures, and 94 posters. Detailed, active, and lively discussions were covered by the following themes: - innovative chiral and achiral compounds - innovative bio- and biobased materials and composites - innovative polymers such as conducting, semiconducting ones, supramolecular (supermolecular, dynamers) - innovative energy systems including fuel cells, solar cells, lithium batteries, and supercapacitors - innovative nanomaterials such as 1D, 2D, and 3D nanomaterials - new ceramic materials such as superconductors, electronic, diaelectronic, ferroelectric, piezoelectric, optoelectric, and magnetic materials - new metallic materials including alloys - other novel materials including drugs, perfumes, agricultural chemicals, and photosensitive materials, displaying materials and fine ceramics and - neutron scattering and its application in fundamental and applied research on new materials. The program served to emphasize that novel materials and their preparation are dynamic research areas that are attracting growing interest from researchers, engineers, industries, and policy-makers. Furthermore, novel materials continue to find applications that serve the needs and interests of producers and consumers. A selection of 13 papers based on specially invited presentations at NMSVII/ FCFP-XXI is published in this issue to demonstrate the quality and scope of the themes of this Conference. During the Conference, the role and contributions of this high-level academic platform to novel materials and their synthesis are well realized by the participants, sponsors, and exhibitors. In addition, the organization committee established the Distinguished Award 2011 for Novel Materials and their Synthesis along with IUPAC Prof. Guoxiu Wang (Australia), Dr. Dr. Fusayoshi Masuda (Japan), Prof. Dr. André-Jean Attias (France), and Prof. Bao-Lian Su (Belgium) received the award for their excellent work. The IUPAC Prof. Jiang Novel Materials Youth Prize was awarded to two winners, Prof. Zhibo Li (China) and Dr. Jr-Hau He (Taiwan, China), for the first time. This will next be awarded in 2013. Three winners for the IUPAC Poster Prize were also awarded. Yuping Wu, Shiyou Guan, and Guoxiu Wang Conference Editors [1] Y. P. Wu. Pure Appl. Chem. 78 (10), iii (2006).
Depth-First Search Encoding of RNA Substructures
Publisher: Springer International Publishing
Date: 2016
Estimating itemsets of interest by sampling
Publisher: IEEE
Date: 2001
Attribute Propagation Network for Graph Zero-Shot Learning
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 03-04-2020
Abstract: The goal of zero-shot learning (ZSL) is to train a model to classify s les of classes that were not seen during training. To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes. In this paper, we aim to optimize the attribute space for ZSL by training a propagation mechanism to refine the semantic attributes of each class based on its neighbors and related classes on a graph of classes. We show that the propagated attributes can produce classifiers for zero-shot classes with significantly improved performance in different ZSL settings. The graph of classes is usually free or very cheap to acquire such as WordNet or ImageNet classes. When the graph is not provided, given pre-defined semantic embeddings of the classes, we can learn a mechanism to generate the graph in an end-to-end manner along with the propagation mechanism. However, this graph-aided technique has not been well-explored in the literature. In this paper, we introduce the “attribute propagation network (APNet)”, which is composed of 1) a graph propagation model generating attribute vector for each class and 2) a parameterized nearest neighbor (NN) classifier categorizing an image to the class with the nearest attribute vector to the image's embedding. For better generalization over unseen classes, different from previous methods, we adopt a meta-learning strategy to train the propagation mechanism and the similarity metric for the NN classifier on multiple sub-graphs, each associated with a classification task over a subset of training classes. In experiments with two zero-shot learning settings and five benchmark datasets, APNet achieves either compelling performance or new state-of-the-art results.
An Agent-Based Hybrid System for Microarray Data Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2009
DOI: 10.1109/MIS.2009.92
Knowledge actionability: satisfying technical and business interestingness
Publisher: Inderscience Publishers
Date: 2007
Multi-Space-Mapped SVMs for Multi-Class Classification
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/ICDM.2008.13
Verifying the Payment Authorization in SET Protocol
Publisher: Springer Berlin Heidelberg
Date: 2003
Data Mining Applications in Social Security
Publisher: Springer US
Date: 2009
Combining kNN Imputation and Bootstrap Calibrated
Publisher: IGI Global
Date: 10-2010
Abstract: The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This paper studies a new estimation for missing (or incomplete) data that is a combination of the kNN imputation and bootstrap calibrated EL (Empirical Likelihood). The combination not only releases the burden of seeking a mathematical valid asymptotic theory for the kNN imputation, but also inherits the advantages of the EL method compared to the normal approximation method. Simulation results demonstrate that the bootstrap calibrated EL method performs quite well in estimating confidence intervals for the imputed data with kNN imputation method.
CONFIDENCE METRICS FOR ASSOCIATION RULE MINING
Publisher: Informa UK Limited
Date: 16-10-2009
Introduction to Domain Driven Data Mining
Publisher: Springer US
Date: 2009
Agent-based risk learning for computing systems
Publisher: IEEE Comput. Soc
Date: 2000
Toward databases mining: Pre-processing collected data
Publisher: Informa UK Limited
Date: 05-2003
DOI: 10.1080/713827171
Introduction to the Domain-Drive Data Mining Special Section
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2010
DOI: 10.1109/TKDE.2010.74
EDUA: An efficient algorithm for dynamic database mining
Publisher: Elsevier BV
Date: 07-2007
A Verification Model for Electronic Transaction Protocols
Publisher: Springer Berlin Heidelberg
Date: 2004
Protecting Location Privacy in Spatial Crowdsourcing using Encrypted Data
Publisher: No publisher found
Date: 2017
High Quality 3D Reconstruction of Indoor Environments using RGB-D Sensors
Publisher: IEEE
Date: 06-2017
Exception Mining on Multiple Time Series in Stock Market
Publisher: IEEE
Date: 12-2008
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Publisher: ACM
Date: 20-08-2020
Social Recommendation With Evolutionary Opinion Dynamics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Description and treatment of deadlocks in the Hecodes distributed expert system
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1990
DOI: 10.1109/21.57277
Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support
Publisher: Elsevier BV
Date: 03-2009
A Framework for Relational Link Discovery
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11589990_193
Definition and application of a comprehensive framework for distributed problem solving
Publisher: Springer Berlin Heidelberg
Date: 1996
Customer Activity Sequence Classification for Debt Prevention in Social Security
Publisher: Springer Science and Business Media LLC
Date: 11-2009
Learning Graph Embedding with Adversarial Training Methods
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Exploring Consensus RNA Substructural Patterns Using Subgraph Mining
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Advances in Knowledge Discovery and Data Mining
Publisher: Springer Berlin Heidelberg
Date: 2011
Identifying Interesting Patterns in Multidatabases
Publisher: Springer Berlin Heidelberg
Date: 22-08-2005
DOI: 10.1007/11011620_7
A framework for merging inconsistent beliefs in security protocol analysis
Publisher: IEEE
Date: 2005
DOI: 10.1109/DEEC.2005.1
Identifying Approximate Itemsets of Interets in Large Databases
Publisher: Springer Science and Business Media LLC
Date: 2003
Community detection in complex network based on APT method
Publisher: Elsevier BV
Date: 10-2020
A Comparative Study of Sampling Methods and Algorithms for Imbalanced Time Series Classification
Publisher: Springer Berlin Heidelberg
Date: 2012
Beyond Low-Pass Filtering: Graph Convolutional Networks With Automatic Filtering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Clustering-based Missing Value Imputation for Data Preprocessing
Publisher: IEEE
Date: 08-2006
Intelligent Strategies for Pathway Mining
Publisher: Springer International Publishing
Date: 2014
An ADL-Approach to Specifying and Analyzing Centralized-Mode Architectural Connection
Publisher: Springer Berlin Heidelberg
Date: 2010
Sufficient Statistics Feature Mapping over Deep Boltzmann Machine for Detection
Publisher: IEEE
Date: 08-2014
Investigations on solution synthesis in distributed expert systems
Publisher: IEEE
Date: 1998
Stock data mining through fuzzy genetic algorithm
Publisher: Atlantis Press
Date: 2006
AN AGENT-BASED FRAMEWORK FOR WELL-LOGGING INFORMATION MANAGEMENT
Publisher: WORLD SCIENTIFIC
Date: 04-2003
Temporal constraint satisfaction in matrix method
Publisher: Informa UK Limited
Date: 02-2003
DOI: 10.1080/713827103
Constructing Hybrid Intelligent Systems for Data Mining from Agent Perspectives
Publisher: Springer Berlin Heidelberg
Date: 2004
Proof of the correctness of EMYCIN sequential propagation under conditional independence assumptions
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/69.761668
Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Mining bridging rules between conceptual clusters
Publisher: Springer Science and Business Media LLC
Date: 04-08-2010
An improvement to matchmaking algorithms for middle agents
Publisher: ACM Press
Date: 2002
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
GBKII: An Imputation Method for Missing Values
Publisher: Springer Berlin Heidelberg
Date: 2007
Neural network strategies for solving synthesis problems in non-conflict cases in distributed expert systems
Publisher: Springer Berlin Heidelberg
Date: 1996
HECODES: a framework for HEterogeneous COoperative distributed expert systems
Publisher: Elsevier BV
Date: 05-1991
Missing Value Imputation Based on Data Clustering
Publisher: Springer Berlin Heidelberg
Date: 2008
Bag Constrained Structure Pattern Mining for Multi-Graph Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2014
Cross-View Feature Hashing for Image Retrieval
Publisher: Springer International Publishing
Date: 2016
Modeling location-based user rating profiles for personalized recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 04-2015
DOI: 10.1145/2663356
Abstract: This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items however, LA-LDA supports three classes of location-based ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top- k recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top- k recommendation and the cold start problem.
An Agent-based Soft Computing Society with Applications in Financial Investment Planning
Publisher: Springer Berlin Heidelberg
Date: 2003
Mining frequent serial episodes over uncertain sequence data
Publisher: ACM
Date: 18-03-2013
System Modeling of a Smart-Home Healthy Lifestyle Assistant
Publisher: Springer Berlin Heidelberg
Date: 2013
RiskContra: A Contrastive Approach to Forecast Traffic Risks with Multi-Kernel Networks
Publisher: Springer Nature Switzerland
Date: 2023
Deep neighbor-aware embedding for node clustering in attributed graphs
Publisher: Elsevier BV
Date: 02-2022
Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns
Publisher: Springer Berlin Heidelberg
Date: 2009
An algorithm for plan verification in multiple agent systems
Publisher: Springer Berlin Heidelberg
Date: 1998
DOI: 10.1007/BFB0055026
Cost-Time Sensitive Decision Tree with Missing Values
Publisher: Springer Berlin Heidelberg
Date: 2007
Semantic Analysis of Component-aspect Dynamism for Connector-based Architecture Styles
Publisher: IEEE
Date: 08-2012
Editorial: deep learning for 5G IoT systems
Publisher: Springer Science and Business Media LLC
Date: 19-07-2021
Guest Editors' Introduction: Special Section on Intelligent Data Preparation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2005
Combined Association Rule Mining
Publisher: Springer Berlin Heidelberg
Date: 2008
Noisy but non-malicious user detection in social recommender systems
Publisher: Springer Science and Business Media LLC
Date: 14-04-2012
Result fusion in multi-agent systems based on OWA operator
Publisher: IEEE Comput. Soc
Date: 2000
Carbohydrate-based activated carbon with high surface acidity and basicity for nickel removal from synthetic wastewater
Publisher: Royal Society of Chemistry (RSC)
Date: 2015
DOI: 10.1039/C5RA08987E
Abstract: The feasibility of preparing activated carbon from carbohydrates (glucose, sucrose and starch) with H 3 PO 4 activation was evaluated by comparing its physicochemical properties and Ni( ii ) adsorption ability with a reference Phragmites australis -based activated carbon.
A Ring-Based Architectural Model for Middle Agents in Agent-Based System
Publisher: Springer Berlin Heidelberg
Date: 2003
Domain-Driven, Actionable Knowledge Discovery
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2007
DOI: 10.1109/MIS.2007.67
A Serving Agent for Integrating Soft Computing and Software Agents
Publisher: Springer Berlin Heidelberg
Date: 1999
Encoding probability propagation in belief networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2002
Welcome Message from the Conference Chairs
Publisher: IEEE
Date: 12-2010
DOI: 10.1109/ICDM.2010.4
Measuring Time-Sensitive and Topic-Specific Influence in Social Networks With LSTM and Self-Attention
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: Many algorithms for Knowledge-Based Question Answering (KBQA) depend on semantic parsing, which translates a question to its logical form. When only weak supervision is provided, it is usually necessary to search valid logical forms for model training. However, a complex question typically involves a huge search space, which creates two main problems: 1) the solutions limited by computation time and memory usually reduce the success rate of the search, and 2) spurious logical forms in the search results degrade the quality of training data. These two problems lead to a poorly-trained semantic parsing model. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. With search space constrained by predicted operators, sufficient search paths can be explored, more valid logical forms can be derived, and operators possibly causing spurious logical forms can be avoided. As a result, a larger proportion of questions in a weakly supervised training set are equipped with logical forms, and fewer spurious logical forms are generated. Such high-quality training data directly contributes to a better semantic parsing model. Experimental results on one of the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our approach and deliver a new state-of-the-art performance.
Domain-driven actionable knowledge discovery in the real world
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11731139_96
Dynamic Focus Strategies for Electronic Trade Execution in Limit Order Markets
Publisher: IEEE
Date: 2006
Domain-driven data mining: A practical methodology
Publisher: IGI Global
Date: 10-2006
Abstract: Extant data mining is based on data-driven methodologies. It either views data mining as an autonomous data-driven, trial-and-error process or only analyzes business issues in an isolated, case-by-case manner. As a result, very often the knowledge discovered generally is not interesting to real business needs. Therefore, this article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user preference. The domain-driven data mining consists of a DDID-PD framework that considers key components such as constraint-based context, integrating domain knowledge, human-machine cooperation, in-depth mining, actionability enhancement, and iterative refinement process. We also illustrate some ex les in mining actionable correlations in Australian Stock Exchange, which show that domain-driven data mining has potential to improve further the actionability of patterns for practical use by industry and business.
Semi-supervised Variable Weighting for Clustering
Publisher: Society for Industrial and Applied Mathematics
Date: 28-04-2011
Active Class Discovery and Learning for Networked Data
Publisher: Society for Industrial and Applied Mathematics
Date: 02-05-2013
Cost-Sensitive Classification with k-Nearest Neighbors
Publisher: Springer Berlin Heidelberg
Date: 2013
A platform for dynamic organization of agents in agent-based systems
Publisher: IEEE
Date: 2004
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: A variety of machine learning applications expect to achieve rapid learning from a limited number of labeled data. However, the success of most current models is the result of heavy training on big data. Meta-learning addresses this problem by extracting common knowledge across different tasks that can be quickly adapted to new tasks. However, they do not fully explore weakly-supervised information, which is usually free or cheap to collect. In this paper, we show that weakly-labeled data can significantly improve the performance of meta-learning on few-shot classification. We propose prototype propagation network (PPN) trained on few-shot tasks together with data annotated by coarse-label. Given a category graph of the targeted fine-classes and some weakly-labeled coarse-classes, PPN learns an attention mechanism which propagates the prototype of one class to another on the graph, so that the K-nearest neighbor (KNN) classifier defined on the propagated prototypes results in high accuracy across different few-shot tasks. The training tasks are generated by subgraph s ling, and the training objective is obtained by accumulating the level-wise classification loss on the subgraph. On two benchmarks, PPN significantly outperforms most recent few-shot learning methods in different settings, even when they are also allowed to train on weakly-labeled data.
A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules
Publisher: Elsevier BV
Date: 08-2004
Exploring Heterogeneous Product Networks for Discovering Collective Marketing Hyping Behavior
Publisher: Springer International Publishing
Date: 2016
Transformation between the EMYCIN model and the Bayesian network
Publisher: Springer Berlin Heidelberg
Date: 1998
DOI: 10.1007/BFB0055030
Information-Based Cooperation in Multiple Agent Systems
Publisher: Springer Berlin Heidelberg
Date: 1999
Anytime mining for multiuser applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2002
Multi-graph Multi-label Learning with Dual-granularity Labeling
Publisher: ACM
Date: 14-08-2021
Graph ensemble boosting for imbalanced noisy graph stream classification.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
A time framework for temporal databases
Publisher: IEEE
Date: 1997
Active Learning without Knowing Individual Instance Labels: A Pairwise Label Homogeneity Query Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
A Human-Friendly MAS for Mining Stock Data
Publisher: IEEE
Date: 12-2006
Intelligence Metasynthesis in Building Business Intelligence Systems
Publisher: Springer Berlin Heidelberg
Date: 2007
Graph stream classification using labeled and unlabeled graphs
Publisher: IEEE
Date: 04-2013
Deep Reinforcement Learning with Transformers for Text Adventure Games
Publisher: IEEE
Date: 08-2020
Shared dynamics learning for large-scale traveling salesman problem
Publisher: Elsevier BV
Date: 04-2023
Database classification for multi-database mining
Publisher: Elsevier BV
Date: 03-2005
IDENTIFYING SOFTWARE COMPONENT ASSOCIATION WITH GENETIC ALGORITHM
Publisher: World Scientific Pub Co Pte Lt
Date: 08-2004
DOI: 10.1142/S0218194004001695
Abstract: Identifying software component association is useful for component management and component retrieval. In this paper we design an evolutionary strategy to understand software structure better and identify software component association, by using genetic algorithm. Our mining strategy is effective for global search, especially when the searched space is so large that it is hardly possible to use deterministic search method.
Combining Support Vector Machines and the t-statistic for Gene Selection in DNA Microarray Data Analysis
Publisher: Springer Berlin Heidelberg
Date: 2010
Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.
JUPPI: A Multi-Level Feature Based Method for PPI Prediction and a Refined Strategy for Performance Assessment
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Finding the best not the most: Regularized loss minimization subgraph selection for graph classification
Publisher: Elsevier BV
Date: 11-2015
Exploring Features for Complicated Objects: Cross-View Feature Selection for Multi-Instance Learning
Publisher: ACM
Date: 03-11-2014
Verifying the Purchase Request in SET Protocol
Publisher: Springer Berlin Heidelberg
Date: 2003
An improved critical diagnosis reasoning method
Publisher: IEEE Comput. Soc. Press
Date: 1996
Enhancing grid-density based clustering for high dimensional data
Publisher: Elsevier BV
Date: 09-2011
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Publisher: IEEE
Date: 11-2021
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks
Publisher: Springer Nature Switzerland
Date: 2023
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Many natural language processing tasks solely rely on sparse dependencies between a few tokens in a sentence. Soft attention mechanisms show promising performance in modeling local/global dependencies by soft probabilities between every two tokens, but they are not effective and efficient when applied to long sentences. By contrast, hard attention mechanisms directly select a subset of tokens but are difficult and inefficient to train due to their combinatorial nature. In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other. In ReSA, a hard attention trims a sequence for a soft self-attention to process, while the soft attention feeds reward signals back to facilitate the training of the hard one. For this purpose, we develop a novel hard attention called "reinforced sequence s ling (RSS)", selecting tokens in parallel and trained via policy gradient. Using two RSS modules, ReSA efficiently extracts the sparse dependencies between each pair of selected tokens. We finally propose an RNN/CNN-free sentence-encoding model, "reinforced self-attention network (ReSAN)", solely based on ReSA. It achieves state-of-the-art performance on both the Stanford Natural Language Inference (SNLI) and the Sentences Involving Compositional Knowledge (SICK) datasets.
Vague One-Class Learning for Data Streams
Publisher: IEEE
Date: 12-2009
DOI: 10.1109/ICDM.2009.70
An Agent-Based Hybrid Intelligent System for Financial Investment Planning
Publisher: Springer Berlin Heidelberg
Date: 2002
Optimized Parameters for Missing Data Imputation
Publisher: Springer Berlin Heidelberg
Date: 2006
Probabilistic Reasoning of Inconsistent Belief in Protocol Analysis
Publisher: Atlantis Press
Date: 2007
Domain-Driven Local Exceptional Pattern Mining for Detecting Stock Price Manipulation
Publisher: Springer Berlin Heidelberg
Date: 2008
Mining Co-locations from Continuously Distributed Uncertain Spatial Data
Publisher: Springer International Publishing
Date: 2016
An approach to digitizing and managing well-logging parameter graphs with agent-based perspective
Publisher: IEEE Comput. Soc
Date: 2003
Semi-parametric optimization for missing data imputation
Publisher: Springer Science and Business Media LLC
Date: 18-01-2007
Improved Consistent Weighted Sampling Revisited
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Efficient Attribute Reduction Based on Discernibility Matrix
Publisher: Springer Berlin Heidelberg
Date: 2007
Efficient approximate entity extraction with edit distance constraints
Publisher: ACM
Date: 29-06-2009
Agent Services - Based infrastructure for online assessment of training strategies
Publisher: IEEE
Date: 2004
Search Efficient Binary Network Embedding
Publisher: Association for Computing Machinery (ACM)
Date: 08-05-2021
DOI: 10.1145/3436892
Abstract: Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned continuous vector representations are inefficient for large-scale similarity search, which often involves finding nearest neighbors measured by distance or similarity in a continuous vector space. In this article, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations using a stochastic gradient descent-based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support faster node similarity search than using Euclidean or other distance measures. Extensive experiments and comparisons demonstrate that BinaryNE not only delivers more than 25 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods. The binary codes learned by BinaryNE also render competitive performance on node classification and node clustering tasks. The source code of the BinaryNE algorithm is available at aokunzhang/BinaryNE.
Verifying the Purchase Request in SET Protocol
Publisher: Springer Berlin Heidelberg
Date: 2003
Summarizing probabilistic frequent patterns
Publisher: ACM
Date: 11-08-2013
Bioinformatics in protein kinases regulatory network and drug discovery
Publisher: Elsevier BV
Date: 04-2015
DOI: 10.1016/J.MBS.2015.01.010
Abstract: Protein kinases have been implicated in a number of diseases, where kinases participate many aspects that control cell growth, movement and death. The deregulated kinase activities and the knowledge of these disorders are of great clinical interest of drug discovery. The most critical issue is the development of safe and efficient disease diagnosis and treatment for less cost and in less time. It is critical to develop innovative approaches that aim at the root cause of a disease, not just its symptoms. Bioinformatics including genetic, genomic, mathematics and computational technologies, has become the most promising option for effective drug discovery, and has showed its potential in early stage of drug-target identification and target validation. It is essential that these aspects are understood and integrated into new methods used in drug discovery for diseases arisen from deregulated kinase activity. This article reviews bioinformatics techniques for protein kinase data management and analysis, kinase pathways and drug targets and describes their potential application in pharma ceutical industry.
Combined Pattern Mining: from Learned Rules to Actionable Knowledge
Publisher: Springer Berlin Heidelberg
Date: 2008
Deep Learning from Noisy Image Labels with Quality Embedding.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Perceiving Environments for Intelligent Agents
Publisher: Springer Berlin Heidelberg
Date: 2000
Mining impact-targeted activity patterns in imbalanced data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2008
Is Frequency Enough for Decision Makers to Make Decisions?
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11731139_58
Integrative Early Requirements Analysis for Agent-Based Software
Publisher: IEEE
Date: 2005
Class Association Rule Mining with Multiple Imbalanced Attributes
Publisher: Springer Berlin Heidelberg
Date: 2007
In-Depth Data Mining and Its Application in Stock Market
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11527503_3
Support vector machines based on K-means clustering for real-time business intelligence systems
Publisher: Inderscience Publishers
Date: 2005
Canonical Consistent Weighted Sampling for Real-Value Weighted Min-Hash
Publisher: IEEE
Date: 12-2016
Using propensity scores to predict the kinases of unannotated phosphopeptides
Publisher: Elsevier BV
Date: 11-2017
Time-Variant Graph Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Semi-supervised Variational Multi-view Anomaly Detection
Publisher: Springer International Publishing
Date: 2021
A Database-Independent Approach of Mining Association Rules with Genetic Algorithm
Publisher: Springer Berlin Heidelberg
Date: 2003
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games
Publisher: Association for Computational Linguistics
Date: 2022
An efficient and practical diagnosis model
Publisher: IEEE
Date: 1998
Mining Better Technical Trading Strategies with Genetic Algorithms
Publisher: IEEE
Date: 12-2006
DOI: 10.1109/AIDM.2006.12
Attribute weighting: How and when does it work for Bayesian Network Classification
Publisher: IEEE
Date: 07-2014
Mining small databases by collecting knowledge
Publisher: IEEE
Date: 04-2001
Knowledge Actionability
Publisher: Springer US
Date: 12-12-2009
Build Emotion Lexicon from Microblogs by Combining Effects of Seed Words and Emoticons in a Heterogeneous Graph
Publisher: WORLD SCIENTIFIC
Date: 03-10-2017
Ubiquitous Intelligence
Publisher: Springer US
Date: 12-12-2009
D 3 M Methodology
Publisher: Springer US
Date: 12-12-2009
Challenges and Trends
Publisher: Springer US
Date: 12-12-2009
PREFACE
Publisher: World Scientific Pub Co Pte Lt
Date: 02-2011
Network Representation Learning: A Survey
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
F-Trade: An Agent-Mining Symbiont for Financial Services
Publisher: ACM
Date: 14-05-2007
Building an Ontology for Financial Investment
Publisher: Springer Berlin Heidelberg
Date: 2000
Guest editors' introduction - Information enhancement for data mining
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2004
An Empirical Evaluation of Bagging with Different Algorithms on Imbalanced Data
Publisher: Springer Berlin Heidelberg
Date: 2011
One-class learning and concept summarization for data streams
Publisher: Springer Science and Business Media LLC
Date: 10-08-2010
Knowledge Discovery in Multiple Databases
Publisher: Springer London
Date: 2004
Domain Driven Data Mining
Publisher: Springer US
Date: 2010
Reasoning with Multimedia Information Using Symbolic Projection
Publisher: Springer Berlin Heidelberg
Date: 2001
Do they belong to the same class
Publisher: ACM
Date: 24-10-2011
Discovering Interesting Association Rules by Clustering
Publisher: Springer Berlin Heidelberg
Date: 2004
MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding
Publisher: Springer International Publishing
Date: 2018
Collecting Quality Data for Database Mining
Publisher: Springer Berlin Heidelberg
Date: 2001
Pattern Discovery in Probabilistic Databases
Publisher: Springer Berlin Heidelberg
Date: 2001
Co-clustering enterprise social networks
Publisher: IEEE
Date: 07-2016
Combined Mining
Publisher: Springer US
Date: 12-12-2009
Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification
Publisher: IEEE
Date: 05-2016
D 3 M AKD Frameworks
Publisher: Springer US
Date: 12-12-2009
An agent-based intelligent system for information gathering from World Wide Web environment
Publisher: IEEE
Date: 2002
Mining Actionable Knowledge on Capital Market Data
Publisher: Springer US
Date: 12-12-2009
Inferring latent network from cascade data for dynamic social recommendation
Publisher: IEEE
Date: 12-2016
Enhancing DWT for Recent-Biased Dimension Reduction of Time Series Data
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11941439_120
Debt Detection in Social Security by Adaptive Sequence Classification
Publisher: Springer Berlin Heidelberg
Date: 2009
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