ORCID Profile
0000-0002-9954-0159
Current Organisation
Queensland University of Technology
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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)
Adaptive Agents and Intelligent Robotics | Aboriginal and Torres Strait Islander Cultural Studies | Historical Studies | Systems biology | Public Health and Health Services | Artificial Intelligence and Image Processing | Microbiology not elsewhere classified | Aboriginal and Torres Strait Islander History | Pattern Recognition and Data Mining | Museum Studies | Computer Vision | Biological physics | Human Geography Not Elsewhere Classified | Health And Community Services | Biological mathematics | Applied mathematics | Social Policy
ANZSRC Socio-Economic Objective (SEO)
The aged | Aboriginal and Torres Strait Islander Development and Welfare | Health related to ageing | Conserving Aboriginal and Torres Strait Islander Heritage | Expanding Knowledge in Engineering | Expanding Knowledge in the Information and Computing Sciences | Other social development and community services | Expanding Knowledge in History and Archaeology |
Related Links
Publications
Learning Consensus and Complementary Information for Multi-aspect Data Clustering
Publisher: Springer International Publishing
Date: 2023
Spectral Clustering on Multi-aspect Data
Publisher: Springer International Publishing
Date: 2023
Subspace Learning for Multi-aspect Data
Publisher: Springer International Publishing
Date: 2023
NMF and Manifold Learning for Multi-aspect Data
Publisher: Springer International Publishing
Date: 2023
Deep Learning-Based Methods for Multi-aspect Data Clustering
Publisher: Springer International Publishing
Date: 2023
A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches
Publisher: Elsevier BV
Date: 10-2023
Machine Learning for Identifying Abusive Content in Text Data
Publisher: Springer International Publishing
Date: 2022
XML Documents Clustering Using Tensor Space Model -- A Preliminary Study
Publisher: IEEE
Date: 12-2010
Data mining the relationship between road crash and skid resistance
Publisher: Springer International Publishing
Date: 29-08-2015
Application of text mining in analysing road crashes for road asset management
Publisher: Springer London
Date: 2010
Tag based collaborative filtering for recommender systems
Publisher: Springer Berlin Heidelberg
Date: 2009
Users segmentations for recommendation
Publisher: ACM
Date: 18-03-2013
A Hybrid Approach of Personalized Web Information Retrieval
Publisher: IEEE
Date: 08-2010
Adaptive Database’s Performance Tuning Based on Reinforcement Learning
Publisher: Springer International Publishing
Date: 2019
Social network analysis of an online dating network
Publisher: ACM
Date: 29-06-2011
Knowledge Discovery over the Deep Web, Semantic Web and XML
Publisher: Springer Berlin Heidelberg
Date: 2009
Parallel streaming signature EM-Tree: A clustering algorithm for web scale applications
Publisher: International World Wide Web Conferences Steering Committee
Date: 18-05-2015
Latent Pattern Identification Using Orthogonal-Constraint Coupled Nonnegative Matrix Factorization
Publisher: Springer International Publishing
Date: 2022
The Ranking based constrained document clustering method and its application to social event detection
Publisher: Springer International Publishing
Date: 2014
Multi-type Relational Data Clustering for Community Detection by Exploiting Content and Structure Information in Social Networks
Publisher: Springer International Publishing
Date: 2019
Improving Recommendation Novelty Based on Topic Taxonomy
Publisher: IEEE
Date: 11-2007
Data mining in Web services discovery and monitoring
Publisher: IGI Global
Date: 2008
Abstract: The business needs, the availability of huge volumes of data and the continuous evolution in Web services functions derive the need of application of data mining in the Web service domain. This article recommends several data mining applications that can leverage problems concerned with the discovery and monitoring of Web services. This article then presents a case study on applying the clustering data mining technique to the Web service usage data to improve the Web service discovery process. This article also discusses the challenges that arise when applying data mining to Web services usage data and abstract informat
Finding Within-Organisation Spatial Information on the Web
Publisher: Springer International Publishing
Date: 2015
Sparsity Constraint Nonnegative Tensor Factorization for Mobility Pattern Mining
Publisher: Springer International Publishing
Date: 2020
Concept Mining in Online Forums Using Self-corpus-Based Augmented Text Clustering
Publisher: Springer International Publishing
Date: 2019
Regularising LSTM classifier by transfer learning for detecting misogynistic tweets with small training set
Publisher: Springer Science and Business Media LLC
Date: 18-06-2020
Can We Define Design? Analyzing Twenty Years of Debate on a Large Email Discussion List
Publisher: Elsevier BV
Date: 2021
Non-negative Matrix Factorization-Based Multi-aspect Data Clustering
Publisher: Springer International Publishing
Date: 2023
Multi-aspect Data Learning: Overview, Challenges and Approaches
Publisher: Springer International Publishing
Date: 2023
FreeS: A fast algorithm to discover frequent free subtrees using a novel canonical form
Publisher: Springer International Publishing
Date: 2015
The Process and Application of XML Data Mining
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-59904-990-8.CH015
Abstract: XML has gained popularity for information representation, exchange and retrieval. As XML material becomes more abundant, its heterogeneity and structural irregularity limit the knowledge that can be gained. The utilisation of data mining techniques becomes essential for improvement in XML document handling. This chapter presents the capabilities and benefits of data mining techniques in the XML domain, as well as, a conceptualization of the XML mining process. It also discusses the techniques that can be applied to XML document structure and/or content for knowledge discovery.
Facilitating and improving the use of web services with data mining
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59904-271-8.CH012
Abstract: Web services have recently received much attention in businesses. However, a number of challenges such as lack of experience in estimating the costs, lack of service innovation and monitoring, and lack of methods for locating appropriate services are to be resolved. One possible approach is by learning from the experiences in Web services and from other similar situations. Such a task requires the use of data mining to represent generalizations on common situations. This chapter examines how some of the issues of Web services can be addressed through data mining.
Semi-supervised document clustering via loci
Publisher: Springer International Publishing
Date: 2015
Transfer Learning via Feature Selection Based Nonnegative Matrix Factorization
Publisher: Springer International Publishing
Date: 2019
A rule-based hybrid method for anomaly detection in online-social-network graphs
Publisher: IEEE
Date: 11-2013
Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering
Publisher: Elsevier BV
Date: 11-2022
PaperMiner—a real-time spatiotemporal visualization for newspaper articles
Publisher: Oxford University Press (OUP)
Date: 28-01-2019
DOI: 10.1093/LLC/FQY084
Personalized recommender system based on item taxonomy and folksonomy
Publisher: ACM
Date: 26-10-2010
Influencing Factors in Achieving Active Ageing
Publisher: IEEE
Date: 12-2006
A Data Mining Application: Analysis of Problems Occurring During a Software Project Development Process
Publisher: World Scientific Pub Co Pte Lt
Date: 08-2005
DOI: 10.1142/S0218194005002476
Abstract: Data mining techniques provide people with new power to research and manipulate the existing large volume of data. A data mining process discovers interesting information from the hidden data that can either be used for future prediction and/or intelligently summarising the details of the data. There are many achievements of applying data mining techniques to various areas such as marketing, medical, and financial, although few of them can be currently seen in software engineering domain. In this paper, a proposed data mining application in software engineering domain is explained and experimented. The empirical results demonstrate the capability of data mining techniques in software engineering domain and the potential benefits in applying data mining to this area.
First, do no harm: automated detection of abusive comments in student evaluation of teaching surveys
Publisher: Informa UK Limited
Date: 13-07-2022
A data analytics case study assessing factors affecting pavement deflection values
Publisher: Inderscience Publishers
Date: 2013
Collaborative filtering recommender systems using tag information
Publisher: IEEE
Date: 12-2008
An interactive predictive data mining system for informed decision
Publisher: Springer Berlin Heidelberg
Date: 2008
Deep learning based topic and sentiment analysis: COVID19 information seeking on social media
Publisher: Springer Science and Business Media LLC
Date: 25-07-2022
DOI: 10.1007/S13278-022-00917-5
Abstract: Social media platforms have become a common place for information exchange among their users. People leave traces of their emotions via text expressions. A systematic collection, analysis, and interpretation of social media data across time and space can give insights into local outbreaks, mental health, and social issues. Such timely insights can help in developing strategies and resources with an appropriate and efficient response. This study analysed a large Spatio-temporal tweet dataset of the Australian sphere related to COVID19. The methodology included a volume analysis, topic modelling, sentiment detection, and semantic brand score to obtain an insight into the COVID19 pandemic outbreak and public discussion in different states and cities of Australia over time. The obtained insights are compared with independently observed phenomena such as government-reported instances.
Personalized Recommender Systems Integrating Social Tags and Item Taxonomy
Publisher: IEEE
Date: 2009
Personalised search - a hybrid approach for web information retrieval and its evaluation
Publisher: Inderscience Publishers
Date: 2011
XML Schema Element Similarity Measures: A Schema Matching Context
Publisher: Springer Berlin Heidelberg
Date: 2009
Generating Predicate Rules from Neural Networks
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11508069_31
DAC: Discriminative Associative Classification
Publisher: Springer Science and Business Media LLC
Date: 17-05-2023
DOI: 10.1007/S42979-023-01819-9
Abstract: In this paper, discriminative associative classification is proposed as a new classification technique based on class discriminative association rules (CDARs). These rules are defined based on discriminative itemsets. The discriminative itemset is frequent in one data class and has much higher frequencies compared with the same itemset in other data classes. The CDAR is a class associative rule (CAR) in one data class that has higher support compared with the same rule in other data classes. Compared to associative classification, there are additional challenges as the Apriori property of the subset is not applicable. The proposed algorithm is designed particularly based on well-defined distinguishing characteristics of the rules, to improve the accuracy and efficiency of the classification in data classes. A novel compact prefix-tree structure is defined for holding the rules in data classes. The empirical analysis shows the effectiveness and efficiency of the proposed method on small and large real datasets.
Identifying differences in wet and dry road crashes using data mining
Publisher: Springer London
Date: 2011
Towards information enrichment through recommendation sharing
Publisher: Springer US
Date: 2009
Extracting point of interest and classifying environment for low sampling crowd sensing smartphone sensor data
Publisher: IEEE
Date: 03-2017
Finding additional semantic entity information for search engines
Publisher: ACM
Date: 05-12-2012
Injury narrative text classification: A preliminary study
Publisher: ACM
Date: 07-11-2014
Understanding the Lifestyle of Older Population: Mobile Crowdsensing Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Discovering cluster evolution patterns with the Cluster Association-aware matrix factorization
Publisher: Springer Science and Business Media LLC
Date: 09-04-2021
Mining discriminative itemsets in data streams using the tilted-time window model
Publisher: Springer Science and Business Media LLC
Date: 15-02-2021
A recommendation method for online dating networks based on social relations and demographic information
Publisher: IEEE
Date: 07-2011
Alternate approach to Time Series reduction
Publisher: IEEE
Date: 02-2018
Discovering Knowledge from XML Documents, in Encyclopedia of Data Warehousing and Mining
Publisher: IGI Global
Date: 2005
DOI: 10.4018/978-1-59140-557-3.CH071
Abstract: XML is the new standard for information exchange and retrieval. An XML document has a schema that defines the data definition and structure of the XML document (Abiteboul et al., 2000). Due to the wide acceptance of XML, a number of techniques are required to retrieve and analyze the vast number of XML documents. Automatic deduction of the structure of XML documents for storing semi-structured data has been an active subject among researchers (Abiteboul et al., 2000 Green et al., 2002). A number of query languages for retrieving data from various XML data sources also has been developed (Abiteboul et al., 2000 W3c, 2004). The use of these query languages is limited (e.g., limited types of inputs and outputs, and users of these languages should know exactly what kinds of information are to be accessed). Data mining, on the other hand, allows the user to search out unknown facts, the information hidden behind the data. It also enables users to pose more complex queries (Dunham, 2003).
Identifying differences in safe roads and crash prone roads using clustering data mining
Publisher: Springer London
Date: 31-07-2014
Do-Rank: DCG optimization for learning-to-rank in tag-based item recommendation systems
Publisher: Springer International Publishing
Date: 2015
A concise social network representation with flow hierarchy using frequent interactions
Publisher: IEEE
Date: 11-2018
A data mining driven risk profiling method for road asset management
Publisher: ACM
Date: 11-08-2013
Unsupervised Visual Time-Series Representation Learning and Clustering
Publisher: Springer International Publishing
Date: 2020
Fine-grained Type Inference in Knowledge Graphs via Probabilistic and Tensor Factorization Methods
Publisher: ACM
Date: 13-05-2019
Machine learning‐based modeling in food processing applications: State of the art
Publisher: Wiley
Date: 04-02-2022
Abstract: Food processing is a complex, multifaceted problem that requires substantial human interaction to optimize the various process parameters to minimize energy consumption and ensure better‐quality products. The development of a machine learning (ML)‐based approach to food processing applications is an exciting and innovative idea for optimizing process parameters and process kinetics to reduce energy consumption, processing time, and ensure better‐quality products however, developing such a novel approach requires significant scientific effort. This paper presents and evaluates ML‐based approaches to various food processing operations such as drying, frying, baking, canning, extrusion, encapsulation, and fermentation to predict process kinetics. A step‐by‐step procedure to develop an ML‐based model and its practical implementation is presented. The key challenges of neural network training and testing algorithms and their limitations are discussed to assist readers in selecting algorithms for solving problems specific to food processing. In addition, this paper presents the potential and challenges of applying ML‐based techniques to hybrid food processing operations. The potential of physics‐informed ML modeling techniques for food processing applications and their strategies is also discussed. It is expected that the potential information of this paper will be valuable in advancing the ML‐based technology for food processing applications.
Investigating semantic measures in XML clustering
Publisher: IEEE
Date: 12-2007
DOI: 10.1109/WI.2006.106
Theoretical model of user acceptance: In the view of measuring success in web personalization
Publisher: Springer Berlin Heidelberg
Date: 2010
Consistency Check between XML Schema and Class Diagram for Document Versioning
Publisher: Insight Society
Date: 04-12-2018
Clustering multi-view data using non-negative matrix factorization and manifold learning for effective understanding: A survey paper
Publisher: Springer International Publishing
Date: 27-11-2019
Automatically acquiring training sets for web information gathering
Publisher: IEEE
Date: 12-2006
DOI: 10.1109/WI.2006.49
Efficient Nonnegative Tensor Factorization via Saturating Coordinate Descent
Publisher: Association for Computing Machinery (ACM)
Date: 30-05-2020
DOI: 10.1145/3385654
Abstract: With the advancements in computing technology and web-based applications, data are increasingly generated in multi-dimensional form. These data are usually sparse due to the presence of a large number of users and fewer user interactions. To deal with this, the Nonnegative Tensor Factorization (NTF) based methods have been widely used. However existing factorization algorithms are not suitable to process in all three conditions of size, density, and rank of the tensor. Consequently, their applicability becomes limited. In this article, we propose a novel fast and efficient NTF algorithm using the element selection approach. We calculate the element importance using Lipschitz continuity and propose a saturation point-based element selection method that chooses a set of elements column-wise for updating to solve the optimization problem. Empirical analysis reveals that the proposed algorithm is scalable in terms of tensor size, density, and rank in comparison to the relevant state-of-the-art algorithms.
An observational analysis of the trope 'A p-value of < 0.05 was considered statistically significant' and other cut-and-paste statistical methods
Publisher: Public Library of Science (PLoS)
Date: 09-03-2022
DOI: 10.1371/JOURNAL.PONE.0264360
Abstract: Appropriate descriptions of statistical methods are essential for evaluating research quality and reproducibility. Despite continued efforts to improve reporting in publications, inadequate descriptions of statistical methods persist. At times, reading statistical methods sections can conjure feelings of dèjá vu , with content resembling cut-and-pasted or “boilerplate text” from already published work. Instances of boilerplate text suggest a mechanistic approach to statistical analysis, where the same default methods are being used and described using standardized text. To investigate the extent of this practice, we analyzed text extracted from published statistical methods sections from PLOS ONE and the Australian and New Zealand Clinical Trials Registry (ANZCTR). Topic modeling was applied to analyze data from 111,731 papers published in PLOS ONE and 9,523 studies registered with the ANZCTR. PLOS ONE topics emphasized definitions of statistical significance, software and descriptive statistics. One in three PLOS ONE papers contained at least 1 sentence that was a direct copy from another paper. 12,675 papers (11%) closely matched to the sentence “a p-value 0.05 was considered statistically significant”. Common topics across ANZCTR studies differentiated between study designs and analysis methods, with matching text found in approximately 3% of sections. Our findings quantify a serious problem affecting the reporting of statistical methods and shed light on perceptions about the communication of statistics as part of the scientific process. Results further emphasize the importance of rigorous statistical review to ensure that adequate descriptions of methods are prioritized over relatively minor details such as p-values and software when reporting research outcomes.
A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model
Publisher: Springer International Publishing
Date: 2014
Evaluation and application of machine learning principles to Zeolite LTA synthesis
Publisher: Elsevier BV
Date: 04-2022
A data mining based method for discovery of web services and their compositions
Publisher: Springer International Publishing
Date: 14-11-2015
Understanding the Spatio-temporal Topic Dynamics of Covid-19 using Nonnegative Tensor Factorization: A Case Study
Publisher: IEEE
Date: 12-2020
Nonnegative coupled matrix tensor factorization for smart city spatiotemporal pattern mining
Publisher: Springer International Publishing
Date: 2019
Wireless technologies to enable electronic business
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-60566-026-4.CH662
Abstract: Research and practices in electronic business (e-business) have witnessed an exponential growth in the last few years (Liautand & Hammond, 2001). Wireless technology has also evolved from simple analog products designed for business use to emerging radioactive, signal-based wireless communications (Shafi, 2001). The tremendous potential of mobile computing and e-business has created a new concept of mobile e-business or e-business over wireless devices (m-business).
Joint Representation Learning with Generative Adversarial Imputation Network for Improved Classification of Longitudinal Data
Publisher: Springer Science and Business Media LLC
Date: 17-10-2023
XML documents clustering by structures
Publisher: Springer-Verlag
Date: 2006
DOI: 10.1007/11766278_33
A novel method for finding similarities between unordered trees using matrix data model
Publisher: Springer Berlin Heidelberg
Date: 2013
Report on INEX 2009
Publisher: Association for Computing Machinery (ACM)
Date: 18-08-2010
Abstract: INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2009 evaluation c aign, which consisted of a wide range of tracks: Ad hoc, Book, Efficiency, Entity Ranking, Interactive, QA, Link the Wiki, and XML Mining. INEX in running entirely on volunteer effort by the IR research community: anyone with an idea and some time to spend, can have a major impact.
HCX: an efficient hybrid clustering approach for XML documents
Publisher: ACM
Date: 16-09-2009
Spatial Information Recognition in Web Documents Using a Semi-supervised Machine Learning Method
Publisher: Springer International Publishing
Date: 2017
Multi-aspect Learning
Publisher: Springer International Publishing
Date: 2023
A Progressive Clustering Algorithm to Group the XML Data by Structural and Semantic Similarity
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2007
DOI: 10.1142/S0218001407005648
Abstract: Since the emergence in the popularity of XML for data representation and exchange over the Web, the distribution of XML documents has rapidly increased. It has become a challenge for researchers to turn these documents into a more useful information utility. In this paper, we introduce a novel clustering algorithm PCXSS that keeps the heterogeneous XML documents into various groups according to their similar structural and semantic representations. We develop a global criterion function CPSim that progressively measures the similarity between a XML document and existing clusters, ignoring the need to compute the similarity between two in idual documents. The experimental analysis shows the method to be fast and accurate.
Adaptive load forecasting using reinforcement learning with database technology
Publisher: Informa UK Limited
Date: 26-03-2019
A Comparative Look at the Resilience of Discriminative and Generative Classifiers to Missing Data in Longitudinal Datasets
Publisher: Springer Nature Singapore
Date: 2022
Improving web database search incorporating users query information
Publisher: ACM
Date: 25-05-2011
A novel learning-to-rank method for automated camera movement control in e-sports spectating
Publisher: Springer Singapore
Date: 2019
Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization
Publisher: Springer Science and Business Media LLC
Date: 15-06-2021
Distributed recommender profiling and selection with gittins indices
Publisher: IEEE
Date: 12-2007
DOI: 10.1109/WI.2006.62
Evolution of the web in artificial intelligence environments
Publisher: Springer Berlin Heidelberg
Date: 2008
A data analytics application assessing pavement deflection factors for a road network
Publisher: ACM
Date: 03-12-2012
Improving matching process in social network using implicit and explicit user information
Publisher: Springer Berlin Heidelberg
Date: 2011
FCMiner: mining functional communities in social networks
Publisher: Springer Science and Business Media LLC
Date: 07-05-2019
Abstracts: The Gerontological Society of America 58th Annual Scientific Meeting November 18-22, 2005 Orlando, FL
Publisher: Oxford University Press (OUP)
Date: 28-08-2010
Mining Discriminative Itemsets Over Data Streams Using Efficient Sliding Window
Publisher: Springer Science and Business Media LLC
Date: 27-06-2023
DOI: 10.1007/S42979-023-01887-X
Abstract: In this paper, we present an efficient novel method for mining discriminative itemsets over data streams using the sliding window model. Discriminative itemsets are the itemsets that are frequent in the target data stream, and their frequency in the target stream is much higher in comparison to their frequency in the rest of the streams. The problem of mining discriminative itemsets has more challenges than mining frequent itemsets, especially in the sliding window model, as during the window frame sliding, the algorithms have to deal with the combinatorial explosion of itemsets in more than one data stream, for the transactions coming in and going out of the sliding window. We propose a single scan algorithm using two novel in-memory data structures for mining discriminative itemsets in a combination of offline and online sliding windows. Offline processing is used for controlling the generation of many unpromising itemsets. Online processing is used for getting more up-to-date and accurate online answers between two offline slidings. The discovered discriminative itemsets are accurately updated in the offline sliding window periodically, and the mining process is continued in the online sliding between two periodic offline slidings. The extensive empirical analysis shows that the proposed algorithm provides efficient time and space complexities with full accuracy. The algorithm can handle large, fast-speed, and complex data streams.
Investigation of Topic Modelling Methods for Understanding the Reports of the Mining Projects in Queensland
Publisher: Springer Singapore
Date: 2021
A people-to-people matching system using graph mining techniques
Publisher: Springer Science and Business Media LLC
Date: 13-02-2014
A Semi-automatic Data Extraction System for Heterogeneous Data Sources: a Case Study from Cotton Industry
Publisher: Springer Singapore
Date: 2021
A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering
Publisher: IEEE
Date: 04-2020
PostMatch: a Framework for Efficient Address Matching
Publisher: Springer Singapore
Date: 2021
Injury narrative text classification using factorization model
Publisher: Springer Science and Business Media LLC
Date: 20-05-2015
How relevant is the irrelevant data: Leveraging the tagging data for a learning-to-rank model
Publisher: ACM
Date: 08-02-2016
Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization
Publisher: Springer Science and Business Media LLC
Date: 30-04-2022
DOI: 10.1007/S41060-022-00324-1
Abstract: The world is witnessing the devastating effects of the COVID-19 pandemic. Each country responded to contain the spread of the virus in the early stages through erse response measures. Interpreting these responses and their patterns globally is essential to inform future responses to COVID-19 variants and future pandemics. A stochastic epidemiological model (SEM) is a well-established mathematical tool that helps to analyse the spread of infectious diseases through communities and the effects of various response measures. However, interpreting the outcome of these models is complex and often requires manual effort. In this paper, we propose a novel method to provide the explainability of an epidemiological model. We represent the output of SEM as a tensor model. We then apply nonnegative tensor factorization (NTF) to identify patterns of global response behaviours of countries and cluster the countries based on these patterns. We interpret the patterns and clusters to understand the global response behaviour of countries in the early stages of the pandemic. Our experimental results demonstrate the advantage of clustering using NTF and provide useful insights into the characteristics of country clusters.
Enhanced Topic Modeling with Multi-modal Representation Learning
Publisher: Springer Nature Switzerland
Date: 2023
Modeling Credit Risk: A Category Theory Perspective
Publisher: MDPI AG
Date: 07-2021
DOI: 10.3390/JRFM14070298
Abstract: This paper proposes a conceptual modeling framework based on category theory that serves as a tool to study common structures underlying erse approaches to modeling credit default that at first sight may appear to have nothing in common. The framework forms the basis for an entropy-based stacking model to address issues of inconsistency and bias in classification performance. Based on the Lending Club’s peer-to-peer loans dataset and Taiwanese credit card clients dataset, relative to in idual base models, the proposed entropy-based stacking model provides more consistent performance across multiple data environments and less biased performance in terms of default classification. The process itself is agnostic to the base models selected and its performance superior, regardless of the models selected.
A people-to-people recommendation system using tensor space models
Publisher: ACM
Date: 26-03-2012
Machine learning for safety hazard identification in construction
Publisher: Routledge
Date: 12-04-2023
A common neighbour based two-way collaborative recommendation method
Publisher: ACM
Date: 26-03-2012
Learning association relationship and accurate geometric structures for multi-type relational data
Publisher: IEEE
Date: 04-2018
Fast and effective clustering of XML data using structural information
Publisher: Springer Science and Business Media LLC
Date: 24-04-2008
MH-DAGMiner: maximal hierarchical sub-DAG mining in directed weighted networks
Publisher: Springer Science and Business Media LLC
Date: 14-12-2018
Analyzing the effectiveness of graph metrics for anomaly detection in online social networks
Publisher: Springer Berlin Heidelberg
Date: 2012
Deep Learning for Bias Detection: From Inception to Deployment
Publisher: Springer Singapore
Date: 2021
Ontology Mining for Semantic Interpretation of Information Needs
Publisher: Springer Berlin Heidelberg
Date: 2007
Exploring Fusion Strategies in Deep Learning Models for Multi-Modal Classification
Publisher: Springer Singapore
Date: 2021
Hybrid neural network and simulated annealing approach to the unit commitment problem
Publisher: Elsevier BV
Date: 08-2000
A social matching system for an online dating network: A preliminary study
Publisher: IEEE
Date: 12-2010
Finding and matching communities in social networks using data mining
Publisher: IEEE
Date: 07-2011
Nonnegative Matrix Factorization to Understand Spatio-Temporal Traffic Pattern Variations During COVID-19: A Case Study
Publisher: Springer Singapore
Date: 2021
Improving matching process in social network
Publisher: IEEE
Date: 12-2010
Effective hybrid recommendation combining users-searches correlations using tensors
Publisher: Springer Berlin Heidelberg
Date: 2011
A Novel Database Exploitation Detection and Privilege Control System Using Data Mining
Publisher: Springer International Publishing
Date: 2018
A Novel Load Forecasting System Leveraging Database Technology
Publisher: Springer International Publishing
Date: 2018
Contextual anomaly detection in spatio-temporal data using locally dense regions
Publisher: IEEE
Date: 11-2018
An Approach to Compress and Represents Time Series Data and Its Application in Electric Power Utilities
Publisher: Springer Singapore
Date: 2019
Evaluation of a hybrid approach of personalized web information retrieval using the FIRE data set
Publisher: ACM
Date: 16-09-2010
Reconstruction of web forms for efficient web search
Publisher: IEEE
Date: 12-2009
Utilising Semantic Tags in XML Clustering
Publisher: Springer Berlin Heidelberg
Date: 2010
Column-Wise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Misogynistic Tweet Detection: Modelling CNN with Small Datasets
Publisher: Springer Singapore
Date: 2019
Utilizing past relations and user similarities in a social matching system
Publisher: Springer Berlin Heidelberg
Date: 2011
Aggregate distance based clustering using Fibonacci series-FIBCLUS
Publisher: Springer Berlin Heidelberg
Date: 2011
Overview of the INEX 2009 XML mining track: Clustering and classification of XML documents
Publisher: Springer Berlin Heidelberg
Date: 2010
Reducing redundancy of test cases generation using code smell detection and refactoring
Publisher: Elsevier BV
Date: 03-2020
A Novel Technique of Using Coupled Matrix and Greedy Coordinate Descent for Multi-view Data Representation
Publisher: Springer International Publishing
Date: 2018
A user driven data mining process model and learning system
Publisher: Springer Berlin Heidelberg
Date: 2008
Combining Schema and Level-Based Matching for Web Service Discovery
Publisher: Springer Berlin Heidelberg
Date: 2010
A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems
Publisher: The Intelligent Networks and Systems Society
Date: 28-02-2021
DOI: 10.22266/IJIES2021.0228.36
Abstract: The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach for exploiting data sparsity in tag-based item recommendation system. We introduce a technique to represent the users’ tag preferences for leveraging the weighted-learning approach. The key idea of the proposed technique comes from the fact that users use different choices of tags to annotate the same item while the same tag may be used to annotate various items in tag-based systems. This points out that users’ tag usage likeliness is different and therefore their tag preferences are also different. We then present three novel weighting schemes that are varied in manners by how the ordinal weighting values are used for labelling the users’ tag preferences. As a result, three weighted tensors are generated based on each scheme. To implement the proposed schemes for generating item recommendations, we develop a novel weighted-learning method called as WRank (Weighted Rank). Our experiments show that considering the users' tag preferences in the tensor-based weightinglearning approach can solve the data sparsity problem as well as improve the quality of recommendation.
XCFS: an XML documents clustering approach using both the structure and the content
Publisher: ACM
Date: 02-11-2009
Expertise analysis in a question answer portal for author ranking
Publisher: IEEE
Date: 12-2008
BOSTER: An Efficient Algorithm for Mining Frequent Unordered Induced Subtrees
Publisher: Springer International Publishing
Date: 2014
Ontology mining for personalized web information gathering
Publisher: IEEE
Date: 11-2007
DOI: 10.1109/WI.2007.82
Discovering Communities with SGNS Modelling-based Network connections and Text communications Clustering
Publisher: IEEE
Date: 12-2020
Generating rules with predicates, terms and variables from the pruned neural networks
Publisher: Elsevier BV
Date: 05-2009
DOI: 10.1016/J.NEUNET.2009.02.001
Abstract: Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate rules. The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.
Efficient mining of discriminative itemsets
Publisher: ACM
Date: 23-08-2017
GAN-IE: Generative Adversarial Network for Information Extraction with Limited Annotated Data
Publisher: Springer Nature Singapore
Date: 2023
Mining discriminative itemsets in data streams
Publisher: Springer International Publishing
Date: 2014
Efficient Outlier Detection in Text Corpus Using Rare Frequency and Ranking
Publisher: Association for Computing Machinery (ACM)
Date: 03-10-2020
DOI: 10.1145/3399712
Abstract: Outlier detection in text data collections has become significant due to the need of finding anomalies in the myriad of text data sources. High feature dimensionality, together with the larger size of these document collections, presents a need for developing accurate outlier detection methods with high efficiency. Traditional outlier detection methods face several challenges including data sparseness, distance concentration, and the presence of a larger number of sub-groups when dealing with text data. In this article, we propose to address these issues by developing novel concepts such as presenting documents with the rare document frequency, finding ranking-based neighborhood for similarity computation, and identifying sub-dense local neighborhoods in high dimensions. To improve the proposed primary method based on rare document frequency, we present several novel ensemble approaches using the ranking concept to reduce the false identifications while finding the higher number of true outliers. Extensive empirical analysis shows that the proposed method and its ensemble variations improve the quality of outlier detection in document repositories as well as they are found scalable compared to the relevant benchmarking methods.
Fine-grained document clustering via ranking and its application to social media analytics
Publisher: Springer Science and Business Media LLC
Date: 07-04-2018
Robust clustering of multi-type relational data via a heterogeneous manifold ensemble
Publisher: IEEE
Date: 04-2015
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
Publisher: IEEE
Date: 12-2020
An Informed Neural Network for Discovering Historical Documentation Assisting the Repatriation of Indigenous Ancestral Human Remains
Publisher: SAGE Publications
Date: 03-2023
DOI: 10.1177/08944393231158788
Abstract: Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating information within scientific and other literature published between 1790 and 1970 documenting their theft, donation, sale, or exchange between institutions. This article reports on collaborative research by data scientists and social science researchers in the Research, Reconcile, Renew Network (RRR) to develop and apply text mining techniques to identify this vital information. We describe our work to date on developing a machine learning-based solution to automate the process of finding and semantically analysing relevant texts. Classification models, particularly deep learning-based models, are known to have low accuracy when trained with small amounts of labelled (i.e. relevant/non-relevant) documents. To improve the accuracy of our detection model, we explore the use of an Informed Neural Network (INN) model that describes documentary content using expert-informed contextual knowledge. Only a few labelled documents are used to provide specificity to the model, using conceptually related keywords identified by RRR experts in provenance research. The results confirm the value of using an INN network model for identifying relevant documents related to the investigation of the global commercial trade in Indigenous human remains. Empirical analysis suggests that this INN model can be generalized for use by other researchers in the social sciences and humanities who want to extract relevant information from large textual corpora.
A novel machine learning approach for database exploitation detection and privilege control
Publisher: Informa UK Limited
Date: 28-01-2019
XML data clustering
Publisher: Association for Computing Machinery (ACM)
Date: 10-2011
Abstract: In the last few years we have observed a proliferation of approaches for clustering XML documents and schemas based on their structure and content. The presence of such a huge amount of approaches is due to the different applications requiring the clustering of XML data. These applications need data in the form of similar contents, tags, paths, structures, and semantics. In this article, we first outline the application contexts in which clustering is useful, then we survey approaches so far proposed relying on the abstract representation of data (instances or schema), on the identified similarity measure, and on the clustering algorithm. In this presentation, we aim to draw a taxonomy in which the current approaches can be classified and compared. We aim at introducing an integrated view that is useful when comparing XML data clustering approaches, when developing a new clustering algorithm, and when implementing an XML clustering component. Finally, the article moves into the description of future trends and research issues that still need to be faced.
Understanding people relationship: Analysis of digitised historical newspaper articles
Publisher: Springer International Publishing
Date: 2015
A semi-supervised graph-based algorithm for detecting outliers in online-social-networks
Publisher: ACM
Date: 18-03-2013
Exploring topic models to discern cyber threats on Twitter: A case study on Log4Shell
Publisher: Elsevier BV
Date: 11-2023
Clustering XML documents using frequent subtrees
Publisher: Springer Berlin Heidelberg
Date: 2009
Grouping people in social networks using a weighted multi-constraints clustering method
Publisher: IEEE
Date: 06-2012
Improve recommendation quality with item taxonomic information
Publisher: Springer Berlin Heidelberg
Date: 2009
Clustering XML Documents Using Closed Frequent Subtrees: A Structural Similarity Approach
Publisher: Springer Berlin Heidelberg
Date: 2008
The hidden web, XML and the Semantic Web
Publisher: ACM
Date: 21-03-2011
Improving Web service discovery by using semantic models
Publisher: Springer Berlin Heidelberg
Date: 2008
XMine: A methodology for mining XML structure
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11610113_74
Evaluating the Performance of XML Document Clustering by Structure Only
Publisher: Springer Berlin Heidelberg
Date: 2007
Active Learning for Effectively Fine-Tuning Transfer Learning to Downstream Task
Publisher: Association for Computing Machinery (ACM)
Date: 11-02-2021
DOI: 10.1145/3446343
Abstract: Language model (LM) has become a common method of transfer learning in Natural Language Processing (NLP) tasks when working with small labeled datasets. An LM is pretrained using an easily available large unlabelled text corpus and is fine-tuned with the labelled data to apply to the target (i.e., downstream) task. As an LM is designed to capture the linguistic aspects of semantics, it can be biased to linguistic features. We argue that exposing an LM model during fine-tuning to instances that capture erse semantic aspects (e.g., topical, linguistic, semantic relations) present in the dataset will improve its performance on the underlying task. We propose a Mixed Aspect S ling (MAS) framework to s le instances that capture different semantic aspects of the dataset and use the ensemble classifier to improve the classification performance. Experimental results show that MAS performs better than random s ling as well as the state-of-the-art active learning models to abuse detection tasks where it is hard to collect the labelled data for building an accurate classifier.
Understanding Urban Spatio-Temporal Usage Patterns Using Matrix Tensor Factorization
Publisher: IEEE
Date: 11-2018
Utilizing the structure and content information for XML document clustering
Publisher: Springer Berlin Heidelberg
Date: 2009
Learning Inter- and intra-manifolds for matrix factorization-based multi-aspect data clustering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Document clustering using incremental and pairwise approaches
Publisher: Springer Berlin Heidelberg
Date: 2008
Assessment of cardiovascular disease risk prediction models: Evaluation methods
Publisher: Springer Berlin Heidelberg
Date: 2011
Deep Learning-Based Approaches for Sentiment Analysis
Publisher: Springer Singapore
Date: 2020
Progressive domain adaptation for detecting hate speech on social media with small training set and its application to COVID-19 concerned posts
Publisher: Springer Science and Business Media LLC
Date: 29-07-2021
Connecting users and items with weighted tags for personalized item recommendations
Publisher: ACM
Date: 13-06-2010
DISSparse: Efficient Mining of Discriminative Itemsets
Publisher: World Scientific Pub Co Pte Ltd
Date: 03-12-2022
DOI: 10.1142/S0219649222500095
Abstract: We tackle the problem of discriminative itemset mining. Given a set of datasets, we want to find the itemsets that are frequent in the target dataset and have much higher frequencies compared with the same itemsets in other datasets. Such itemsets are very useful for dataset discrimination. We demonstrate that this problem has important applications and, at a same time, is very challenging. We present the DISSparse algorithm, a mining method that uses two determinative heuristics based on the sparsity characteristics of the discriminative itemsets as a small subset of the frequent itemsets. We prove that the DISSparse algorithm is sound and complete. We experimentally investigate the performance of the proposed DISSparse on a range of datasets, evaluating its efficiency and stability and demonstrating it is substantially faster than the baseline method.
Corpus-Based Augmented Media Posts with Density-Based Clustering for Community Detection
Publisher: IEEE
Date: 11-2018
Data Replication Optimization Using Simulated Annealing
Publisher: Springer Singapore
Date: 2019
Element similarity measures in XML schema matching
Publisher: Elsevier BV
Date: 12-2010
An Efficient Ranking-Centered Density-Based Document Clustering Method
Publisher: Springer International Publishing
Date: 2018
Overview of the INEX 2010 XML Mining Track: Clustering and Classification of XML Documents
Publisher: Springer Berlin Heidelberg
Date: 2011
Two-way recommendation methods for social networks
Publisher: ACM
Date: 03-11-2014
XML schema clustering with semantic and hierarchical similarity measures
Publisher: Elsevier BV
Date: 05-2007
Machine learning for predicting propensity-to-pay energy bills
Publisher: Elsevier BV
Date: 02-2023
XML Documents Clustering Using a Tensor Space Model
Publisher: Springer Berlin Heidelberg
Date: 2011
Leveraging the network information for evaluating answer quality in a collaborative question answering portal
Publisher: Springer Science and Business Media LLC
Date: 11-01-2012
Semantics-based web service discovery using information retrieval techniques
Publisher: Springer Berlin Heidelberg
Date: 2011
Deep Hierarchical Non-negative Matrix Factorization for Clustering Short Text
Publisher: Springer International Publishing
Date: 2020
Recommender System Framework Using Clustering and Collaborative Filtering
Publisher: IEEE
Date: 11-2010
The heterogeneous cluster ensemble method using hubness for clustering text documents
Publisher: Springer Berlin Heidelberg
Date: 2013
Discovering influence hierarchy based on frequent social interactions
Publisher: IEEE
Date: 08-2018
Innovations in Web applications by using the Artificial Intelligence Paradigm
Publisher: Springer Berlin Heidelberg
Date: 2008
An introduction to the evolution of the Web in an artificial intelligence environment
Publisher: Springer Berlin Heidelberg
Date: 2008
People to People Recommendation using Coupled Nonnegative Boolean Matrix Factorization
Publisher: IEEE
Date: 02-2018
Adaptive Data Replication Optimization Based on Reinforcement Learning
Publisher: IEEE
Date: 12-2020
BEST: An efficient algorithm for mining frequent unordered embedded subtrees
Publisher: Springer International Publishing
Date: 2014
A reciprocal collaborative method using relevance feedback and feature importance
Publisher: IEEE
Date: 11-2013
An efficient neighbourhood estimation technique for making recommendations
Publisher: Springer Berlin Heidelberg
Date: 2009
Tensor-based Item recommendation using probabilistic ranking in social tagging systems
Publisher: ACM
Date: 07-04-2014
XCLS: A fast and effective clustering algorithm for heterogenous XML documents
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11731139_35
NOCOL - Nonnegative Orthogonal Constraint Outlier Learning
Publisher: Springer International Publishing
Date: 2021
Link-the-wiki: Performance evaluation based on frequent phrases
Publisher: Springer Berlin Heidelberg
Date: 2009
Identifying Points of Interest for Elderly in Singapore through Mobile Crowdsensing
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2017
Dynamic Query Expansion for Efficient Information Retrieval
Publisher: IEEE
Date: 10-2010
Thai word segmentation with hidden markov model and decision tree
Publisher: Springer Berlin Heidelberg
Date: 2009
A recommendation approach dealing with multiple market segments
Publisher: IEEE
Date: 11-2013
Road crash proneness prediction using data mining
Publisher: ACM
Date: 21-03-2011
An ontology-based framework for knowledge retrieval
Publisher: IEEE
Date: 12-2008
Clustering and labeling a web scale document collection using wikipedia clusters
Publisher: ACM
Date: 03-11-2014
Discovering interesting information with advances in web technology
Publisher: Association for Computing Machinery (ACM)
Date: 30-04-2013
Abstract: The Web is a steadily evolving resource comprising much more than mere HTML pages. With its ever-growing data sources in a variety of formats, it provides great potential for knowledge discovery. In this article, we shed light on some interesting phenomena of the Web: the deep Web, which surfaces database records as Web pages the Semantic Web, which defines meaningful data exchange formats XML, which has established itself as a lingua franca for Web data exchange and domain-specific markup languages, which are designed based on XML syntax with the goal of preserving semantics in targeted domains. We detail these four developments in Web technology, and explain how they can be used for data mining. Our goal is to show that all these areas can be as useful for knowledge discovery as the HTML-based part of the Web.
Data Driven Encoding of Structures and Link Predictions in Large XML Document Collections
Publisher: IGI Global
Date: 2011
Exploiting item taxonomy for solving cold-start problem in recommendation making
Publisher: IEEE
Date: 11-2008
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