John Roddick

Neighborhood Discriminant Nearest Feature Line Analysis for Face Recognition

Publisher: IEEE

Date: 12-2011

DOI: 10.1109/IBICA.2011.91

FP-GraphMiner-A Fast Frequent Pattern Mining Algorithm for Network Graphs

Publisher: Journal of Graph Algorithms and Applications

Date: 2011

DOI: 10.7155/JGAA.00247

Do changes in weight status affect cognitive function in children and adolescents with obesity? A secondary analysis of a clinical trial

Publisher: BMJ

Date: 02-2019

DOI: 10.1136/BMJOPEN-2018-021586

Abstract: It is unclear whether an association exists between obesity in children/adolescents and cognitive function, and whether the latter can be altered by body mass index (BMI) standard deviation score (SDS) reductions. We aimed to determine whether an association exists between BMI SDS and cognitive function in children/adolescents with obesity engaged in an obesity intervention. Second, we sought to determine if BMI SDS reduction at 12 months was associated with improved cognitive function. Secondary analysis of a clinical trial. Participants (n=69) were recruited from an obesity intervention. Eligible participants (recruited June 2013 to June 2015) were aged 6–16 years, with a BMI ≥98th centile or BMI st centile with weight-related comorbidities. Primary outcome measure was change in BMI SDS from baseline at 12 months. Dependent variables of cognitive functioning and school achievement were assessed at baseline and 12 months, using dependent variables of cognitive functioning (elements of Ravens Standard Progressive Matrices, Wide Range Achievement Test-fourth edition and Wechsler Intelligence Scale for Children-fourth edition). At baseline, BMI SDS was not associated with all aspects of cognitive function tested (n=69). Reductions in BMI SDS over time did not alter cognitive function overall. However, there was a greater reduction in comprehension standard scores in participants who increased their BMI SDS (adjusted estimated difference −6.1, 95% CI −11.6 to −0.6 p=0.03). There were no observed associations between BMI SDS and cognitive function in participants, apart from comprehension in the exploratory analyses, which may have been a random finding. Further studies need to include larger longitudinal cohorts incorporating a wider BMI range at entry to determine whether our findings persist. ANZCTR12611000862943 Pre-results.

A taxonomy for schema versioning based on the relational and Entity Relationship Models

Publisher: Springer-Verlag

Date: 1994

DOI: 10.1007/BFB0024363

Sparse representation-based MRI super-resolution reconstruction

Publisher: Elsevier BV

Date: 2014

DOI: 10.1016/J.MEASUREMENT.2013.10.026

Distortion-Based Heuristic Sensitive Rule Hiding Method – The Greedy Way

Publisher: Springer International Publishing

Date: 2014

DOI: 10.1007/978-3-319-07455-9_9

Transaction Time Support

Publisher: Springer US

Date: 1995

DOI: 10.1007/978-1-4615-2289-8_17

Temporal Vacuuming

Publisher: Springer US

Date: 2009

DOI: 10.1007/978-0-387-39940-9_1045

A TSQL2 tutorial

Publisher: Association for Computing Machinery (ACM)

Date: 09-1994

DOI: 10.1145/187436.187449

An analysis of application rates to programs in information technology, science, and engineering

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2001

DOI: 10.1109/13.940999

Relay Node Selection Strategy for Wireless Sensor Network

Publisher: Springer International Publishing

Date: 24-09-2017

DOI: 10.1007/978-3-319-68527-4_25

Temporal, Spatial, and Spatio-Temporal Data Mining

Publisher: Springer Berlin Heidelberg

Date: 2001

DOI: 10.1007/3-540-45244-3

Hide Association Rules with Fewer Side Effects

Publisher: Institute of Electronics, Information and Communications Engineers (IEICE)

Date: 2015

DOI: 10.1587/TRANSINF.2014EDP7345

Approximate Query Processing Model for Mobile Computing

Publisher: Springer US

Date: 2000

DOI: 10.1007/978-1-4615-1379-7_15

What's interesting about Cricket?

Publisher: Association for Computing Machinery (ACM)

Date: 07-2001

DOI: 10.1145/507533.507535

Abstract: Despite significant progress, determining the interestingness of a rule remains a difficult problem. This short paper investigates the lessons that may be learned from analysing the (largely manual) selection of interesting statistics for cricket (or any other data rich sport) by experts. In particular, the effect of thresholds on the interestingness of rules describing events in the sporting arena is discussed. The concept of anticipation is shown also to be critical in this selection and to vary the level of interest in events that may contribute to the achievement of a threshold value during a match, thus adding a temporal dimension to interestingness. This temporal aspect can be best modelled on the single-past-branching-future model of time. As a result of this investigation, a few new general ideas are discussed that add to the research in this area. Significantly, some of the new criteria are implicitly temporal in that they rely on a model of behaviour over time. The applicability of threshold values for detecting uncharacteristically poor performances are canvassed as areas of interest yet to be explored.

Accommodating mesodata into conceptual modelling methodologies

Publisher: Elsevier BV

Date: 04-2008

DOI: 10.1016/J.INFSOF.2007.05.001

Power Reduction of Wireless Sensor Networks Using Ant Colony Optimization

Publisher: IEEE

Date: 09-2010

DOI: 10.1109/CASON.2010.110

A survey of temporal knowledge discovery paradigms and methods

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 07-2002

DOI: 10.1109/TKDE.2002.1019212

Association mining

Publisher: Association for Computing Machinery (ACM)

Date: 25-07-2006

DOI: 10.1145/1132956.1132958

Abstract: The task of finding correlations between items in a dataset, association mining, has received considerable attention over the last decade. This article presents a survey of association mining fundamentals, detailing the evolution of association mining algorithms from the seminal to the state-of-the-art. This survey focuses on the fundamental principles of association mining, that is, itemset identification, rule generation, and their generic optimizations.

Survey of spatio-temporal databases

Publisher: Springer Science and Business Media LLC

Date: 1999

DOI: 10.1023/A:1009800916313

An Efficient K-Medoids-Based Algorithm Using Previous Medoid Index, Triangular Inequality Elimination Criteria, and Partial Distance Search

Publisher: Springer Berlin Heidelberg

Date: 2002

DOI: 10.1007/3-540-46145-0_7

Incorporating ontology-based semantics into conceptual modelling

Publisher: Elsevier BV

Date: 08-2015

DOI: 10.1016/J.IS.2015.02.003

The TSQL2 Temporal Query Language

Publisher: Springer US

Date: 1995

DOI: 10.1007/978-1-4615-2289-8

In Support of Mesodata in Database Management Systems

Publisher: Springer Berlin Heidelberg

Date: 2004

DOI: 10.1007/978-3-540-30075-5_64

Conceptual Modeling for Advanced Application Domains

Publisher: Springer Berlin Heidelberg

Date: 2004

DOI: 10.1007/B101694

Handling discovered structure in database systems

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 04-1996

DOI: 10.1109/69.494163

Exploratory medical knowledge discovery

Publisher: Association for Computing Machinery (ACM)

Date: 07-2003

DOI: 10.1145/959242.959243

Abstract: The application of data mining and knowledge discovery techniques to medical and health datasets is a rewarding but highly challenging area. Not only are the datasets large, complex, heterogeneous, hierarchical, time-varying and of varying quality but there exists asubstantial medical knowledge base which demands a robust collaboration between the data miner and the health professional(s) if useful information is to be extracted.This paper presents the experiences of the authors and others in applying exploratory data mining techniques to medical, health and clinical data. In so doing, it elicits a number of general issues and provides pointers to possible areas of future research in data mining and knowledge discovery more broadly.

Schema Versioning

Publisher: Springer US

Date: 1995

DOI: 10.1007/978-1-4615-2289-8_22

A consensus glossary of temporal database concepts

Publisher: Association for Computing Machinery (ACM)

Date: 03-1994

DOI: 10.1145/181550.181560

Abstract: This document contains definitions of a wide range of concepts specific to and widely used within temporal databases. In addition to providing definitions, the document also includes separate explanations of many of the defined concepts. Two sets of criteria are included. First, all included concepts were required to satisfy four relevance criteria, and, second, the naming of the concepts was resolved using a set of evaluation criteria. The concepts are grouped into three categories: concepts of general database interest, of temporal database interest, and of specialized interest. This document is a digest of a full version of the glossary 1 . In addition to the material included here, the full version includes substantial discussions of the naming of the concepts.The consensus effort that lead to this glossary was initiated in Early 1992. Earlier status documents appeared in March 1993 and December 1992 and included terms proposed after an initial glossary appeared in SIGMOD Record in September 1992. The present glossary subsumes all the previous documents. It was most recently discussed at the "ARPA/NSF International Workshop on an Infrastructure for Temporal Databases," in Arlington, TX, June 1993, and is recommended by a significant part of the temporal database community. The glossary meets a need for creating a higher degree of consensus on the definition and naming of temporal database concepts.

A survey of schema versioning issues for database systems

Publisher: Elsevier BV

Date: 1995

DOI: 10.1016/0950-5849(95)91494-K

Orientation Tree Structure Based Wavelet/VQ Multiple Description Coding

Publisher: IEEE

Date: 09-2010

DOI: 10.1109/PCSPA.2010.174

Visualising hierarchical associations

Publisher: Springer Science and Business Media LLC

Date: 30-09-2004

DOI: 10.1007/S10115-003-0139-0

Guiding Knowledge Discovery Through Interactive Data Mining

Publisher: IGI Global

Date: 2003

DOI: 10.4018/978-1-59140-057-8.CH004

Abstract: Knowledge discovery is the process of eliciting interesting knowledge from data repositories. Due to the inability of computers to understand abstract concepts, present mining algorithms do not adequately constrain the generation of rules to those that are of interest to the user. Interactive mining techniques aim to alleviate this problem by involving the user in the mining process, so that the user’s understanding of abstract semantic concepts and domain knowledge can guide the discovery process, resulting in accelerated mining with improved results. This chapter presents a discussion of the current state of interactive data mining research.

Paradigms for spatial and spatio-temporal data mining

Publisher: Taylor & Francis

Date: 2001

DOI: 10.4324/9780203468029_CHAPTER_2

Database Issues in Knowledge Discovery and Data Mining

Publisher: Australian Journal of Information Systems

Date: 05-1999

DOI: 10.3127/AJIS.V6I2.310

Postponing Schema Definition: Low Instance-to-Entity Ratio (LItER) Modelling

Publisher: Springer Berlin Heidelberg

Date: 2007

DOI: 10.1007/978-3-540-77503-4_16

A note on using the resistance-distance matrix to solve Hamiltonian cycle problem

Publisher: Springer Science and Business Media LLC

Date: 28-06-2017

DOI: 10.1007/S10479-017-2571-7

Directional Shuffled Frog Leaping Algorithm

Publisher: Springer International Publishing

Date: 03-11-2017

DOI: 10.1007/978-3-319-70730-3_31

TSQL2 language specification

Publisher: Association for Computing Machinery (ACM)

Date: 03-1994

DOI: 10.1145/181550.181562

Beyond schema versioning: A flexible model for spatio-temporal schema selection

Publisher: Springer Science and Business Media LLC

Date: 2001

DOI: 10.1023/A:1011407904714

A bibliography of temporal, spatial and spatio-temporal data mining research

Publisher: Association for Computing Machinery (ACM)

Date: 06-1999

DOI: 10.1145/846170.846173

Abstract: With the growth in the size of datasets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, a greater recognition of the value of temporal and spatial data has been evident and the first papers looking at the confluence of these two areas are starting to emerge. This short paper provides a few comments on this research and provides a bibliography of relevant research papers investigating temporal, spatial and spatio-temporal data mining.

A Research on Behavior of Sleepy Lizards Based on KNN Algorithm

Publisher: Springer Berlin Heidelberg

Date: 2012

DOI: 10.1007/978-3-642-28490-8_12

The case for mesodata: An empirical investigation of an evolving database system

Publisher: Elsevier BV

Date: 09-2007

DOI: 10.1016/J.INFSOF.2006.11.001

Information Organization and Databases

Publisher: Springer US

Date: 2000

DOI: 10.1007/978-1-4615-1379-7

Directional Discriminant Analysis Based on Nearest Feature Line

Publisher: Springer Berlin Heidelberg

Date: 2012

DOI: 10.1007/978-3-642-28490-8_13

A Tutorial on Meta-Heuristics for Optimization

Publisher: WORLD SCIENTIFIC

Date: 02-2004

DOI: 10.1142/9789812562524_0004

A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal Patterns

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 06-2008

DOI: 10.1109/TKDE.2008.20

Temporal Interval Logic in Data Mining

Publisher: Springer Berlin Heidelberg

Date: 2000

DOI: 10.1007/3-540-44533-1_87

Opportunities for Knowledge Discovery in Spatio-Temporal Information Systems

Publisher: Australian Journal of Information Systems

Date: 05-1998

DOI: 10.3127/AJIS.V5I2.338

Towards an accommodation of delay in temporal active databases

Publisher: IEEE Comput. Soc

Date: 2000

DOI: 10.1109/ADC.2000.819821

Methods and Interpretation of Database Summarisation

Publisher: Springer Berlin Heidelberg

Date: 1999

DOI: 10.1007/3-540-48309-8_56

Kernel self-optimization learning for kernel-based feature extraction and recognition

Publisher: Elsevier BV

Date: 02-2014

DOI: 10.1016/J.INS.2013.08.011

Discovering Richer Temporal Association Rules from Interval-Based Data

Publisher: Springer Berlin Heidelberg

Date: 2005

DOI: 10.1007/11546849_31

Advances in Conceptual Modeling

Publisher: Springer Berlin Heidelberg

Date: 1999

DOI: 10.1007/3-540-48054-4

Solving Constrained Optimization Problems by an Improved Particle Swarm Optimization

Publisher: IEEE

Date: 12-2011

DOI: 10.1109/IBICA.2011.35

GAM: a guidance enabled association mining environment

Publisher: Inderscience Publishers

Date: 2007

DOI: 10.1504/IJBIDM.2007.012944

A Multi-level Framework for the Analysis of Sequential Data

Publisher: Springer Berlin Heidelberg

Date: 2006

DOI: 10.1007/11677437_18

Local nulls in summarised mobile and distributed databases

Publisher: WIT Press

Date: 27-06-2006

DOI: 10.2495/DATA060411

Schema evolution in database systems

Publisher: Association for Computing Machinery (ACM)

Date: 12-1992

DOI: 10.1145/141818.141826

Abstract: Schema Evolution is the ability of a database system to respond to changes in the real world by allowing the schema to evolve. In many systems this property also implies a retaining of past states of the schema. This latter property is necessary if data recorded during the lifetime of one version of the schema is not to be made obsolete as the schema changes. This annotated bibliography investigates current published research with respect to the handling of changing schemas in database systems.

ARMADA – An algorithm for discovering richer relative temporal association rules from interval-based data

Publisher: Elsevier BV

Date: 10-2007

DOI: 10.1016/J.DATAK.2006.10.009

Legal and technical issues of privacy preservation in data mining

Publisher: IGI Global

Date: 2009

DOI: 10.4018/978-1-60566-010-3.CH180

Abstract: To paraphrase Winograd (1992), we bring to our communities a tacit comprehension of right and wrong that makes social responsibility an intrinsic part of our culture. Our ethics are the moral principles we use to assert social responsibility and to perpetuate safe and just societies. Moreover, the introduction of new technologies can have a profound effect on our ethical principles. The emergence of very large databases, and the associated automated data analysis tools, present yet another set of ethical challenges to consider. Socio-ethical issues have been identified as pertinent to data mining and there is a growing concern regarding the (ab)use of sensitive information (Clarke, 1999 Clifton et al., 2002 Clifton and Estivill-Castro, 2002 Gehrke, 2002). Estivill-Castro et al., discuss surveys regarding public opinion on personal privacy that show a raised level of concern about the use of private information (Estivill-Castro et al., 1999). There is some justification for this concern a 2001 survey in InfoWeek found that over 20% of companies store customer data with information about medical profile and/or customer demographics with salary and credit information, and over 15% store information about customers’ legal histories.

Privacy preservation through a greedy, distortion-based rule-hiding method

Publisher: Springer Science and Business Media LLC

Date: 05-05-2015

DOI: 10.1007/S10489-015-0671-0

SQL/SE

Publisher: Association for Computing Machinery (ACM)

Date: 09-1992

DOI: 10.1145/140979.140985

Abstract: The incorporation of a knowledge of time within database systems allows for temporally related information to be modelled more naturally and consistently. Adding this support to the metadatabase further enhances its semantic capability and allows elaborate interrogation of data. This paper presents SQL/SE, an SQL extension capable of handling schema evolution in relational database systems.

An Effective Image Enhancement Method for Electronic Portal Images

Publisher: Springer Berlin Heidelberg

Date: 2010

DOI: 10.1007/978-3-642-16696-9_19

Spatial, temporal and spatio-temporal databases - hot issues and directions for phd research

Publisher: Association for Computing Machinery (ACM)

Date: 06-2004

DOI: 10.1145/1024694.1024724

Abstract: Spatial and temporal database systems, both in theory and in practice, have developed dramatically over the past two decades to the point where usable commercial systems, underpinned by a robust theoretical foundation, are now starting to appear. While much remains to be done, topics for research must be chosen carefully to avoid embarking on impractical or unprofitable areas. This is particularly true for doctoral research where the candidate must build a tangible contribution in a relatively short time.The panel session at the Eighth International Symposium on Spatial and Temporal Databases (SSTD 2003) held on Santorini Island, Greece [7] in July 2003 thus took as its focus the question What to focus on (and what to avoid) in Spatial and Temporal Databases: recommendations for doctoral research. This short paper, authored by the panel members, summarizes these discussions.

A Novel Approach on Behavior of Sleepy Lizards Based on K-Nearest Neighbor Algorithm

Publisher: Springer International Publishing

Date: 10-12-2013

DOI: 10.1007/978-3-319-02993-1_13

Improved search strategies and extensions to k-medoids-based clustering algorithms

Publisher: Inderscience Publishers

Date: 2008

DOI: 10.1504/IJBIDM.2008.020520

GOPT Resolution

Publisher: Inderscience Publishers

Date: 2011

DOI: 10.1504/IJAISC.2011.042714

Mining Relationships Between Interacting Episodes

Publisher: Society for Industrial and Applied Mathematics

Date: 22-04-2004

DOI: 10.1137/1.9781611972740.1

A Reduce Identical Event Transmission Algorithm for Wireless Sensor Networks

Publisher: Springer Berlin Heidelberg

Date: 17-07-2012

DOI: 10.1007/978-3-642-31603-6_13

Sequential pattern mining -- approaches and algorithms

Publisher: Association for Computing Machinery (ACM)

Date: 02-2013

DOI: 10.1145/2431211.2431218

Abstract: Sequences of events, items, or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyze frequent subsequences is a common problem. Sequential Pattern Mining arose as a subfield of data mining to focus on this field. This article surveys the approaches and algorithms proposed to date.

Social Networks: A Framework of Computational Intelligence

Publisher: Springer International Publishing

Date: 2014

DOI: 10.1007/978-3-319-02993-1

Adding Temporal Semantics to Association Rules

Publisher: Springer Berlin Heidelberg

Date: 1999

DOI: 10.1007/978-3-540-48247-5_65

Image Recognition Based on Kernel Self-Optimized Learning

Publisher: IEEE

Date: 12-2011

DOI: 10.1109/IBICA.2011.23

Incremental Meta-Mining from Large Temporal Data Sets

Publisher: Springer Berlin Heidelberg

Date: 1999

DOI: 10.1007/978-3-540-49121-7_4

QUATRE Algorithm with Sort Strategy for Global Optimization in Comparison with DE and PSO Variants

Publisher: Springer International Publishing

Date: 24-09-2017

DOI: 10.1007/978-3-319-68527-4_34

Schema Evolution

Publisher: Springer US

Date: 2009

DOI: 10.1007/978-0-387-39940-9_1532

Ant colony system with communication strategies

Publisher: Elsevier BV

Date: 12-2004

DOI: 10.1016/J.INS.2003.10.013

Parallel Ant Colony Systems

Publisher: Springer Berlin Heidelberg

Date: 2003

DOI: 10.1007/978-3-540-39592-8_38

Overview of Algorithms for Swarm Intelligence

Publisher: Springer Berlin Heidelberg

Date: 2011

DOI: 10.1007/978-3-642-23935-9_3

RPL: a ruleset pattern language

Publisher: WIT Press

Date: 2015

DOI: 10.2495/AIIA140511

Gabor-based kernel self-optimization Fisher discriminant for optical character segmentation from text-image-mixed document

Publisher: Elsevier BV

Date: 11-2015

DOI: 10.1016/J.IJLEO.2015.07.090

Linear temporal sequences and their interpretation using midpoint relationships

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2005

DOI: 10.1109/TKDE.2005.12

Facilitating Database Attribute Domain Evolution Using Mesodata

Publisher: Springer Berlin Heidelberg

Date: 2004

DOI: 10.1007/978-3-540-30466-1_39

Announcement—the temporal query language TSQL2 final language definition

Publisher: Association for Computing Machinery (ACM)

Date: 09-1994

DOI: 10.1145/187436.187454

Temporal semantics in information systems—A survey

Publisher: Elsevier BV

Date: 05-1992

DOI: 10.1016/0306-4379(92)90016-G

Lattice-structured Domains, Imperfect Data and Inductive Queries

Publisher: Springer Berlin Heidelberg

Date: 2000

DOI: 10.1007/3-540-44469-6_62

Schema Vacuuming in Temporal Databases

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 05-2009

DOI: 10.1109/TKDE.2008.201

Schema Versioning

Publisher: Springer US

Date: 2009

DOI: 10.1007/978-0-387-39940-9_323

Advances and Research Directions in Data-Warehousing Technology

Publisher: Australian Journal of Information Systems

Date: 11-1999

DOI: 10.3127/AJIS.V7I1.287

An Evaluation of HTM and LSTM for Short-Term Arterial Traffic Flow Prediction

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 05-2019

DOI: 10.1109/TITS.2018.2843349

Towards polymorphic relationships in the entity-relationship model

Publisher: WIT Press

Date: 2015

DOI: 10.2495/CSSE140341

Lizard movement tracks: variation in path re-use behaviour is consistent with a scent-marking function

Publisher: PeerJ

Date: 22-03-2016

DOI: 10.7717/PEERJ.1844

Abstract: In idual movement influences the spatial and social structuring of a population. Animals regularly use the same paths to move efficiently to familiar places, or to patrol and mark home ranges. We found that Australian sleepy lizards ( Tiliqua rugosa ), a monogamous species with stable pair-bonds, repeatedly used the same paths within their home ranges and investigated whether path re-use functions as a scent-marking behaviour, or whether it is influenced by site familiarity. Lizards can leave scent trails on the substrate when moving through the environment and have a well-developed vomeronasal system to detect and respond to those scents. Path re-use would allow sleepy lizards to concentrate scent marks along these well-used trails, advertising their presence. Hypotheses of mate attraction and mating competition predict that sleepy lizard males, which experience greater intra-sexual competition, mark more strongly. Consistent with those hypotheses, males re-used their paths more than females, and lizards that showed pairing behaviour with in iduals of the opposite sex re-used paths more than unpaired lizards, particularly among females. Hinterland marking is most economic when home ranges are large and mobility is low, as is the case in the sleepy lizard. Consistent with this strategy, re-used paths were predominantly located in the inner 50% home range areas. Together, our detailed movement analyses suggest that path re-use is a scent marking behaviour in the sleepy lizard. We also investigated but found less support for alternative explanations of path re-use behaviour, such as site familiarity and spatial knowledge. Lizards established the same number of paths, and used them as often, whether they had occupied their home ranges for one or for more years. We discuss our findings in relation to maintenance of the monogamous mating system of this species, and the spatial and social structuring of the population.

Reduce, Reuse, Recycle : Practical Approaches to Schema Integration, Evolution and Versioning

Publisher: Springer Berlin Heidelberg

Date: 2006

DOI: 10.1007/11908883_26

Higher order mining

Publisher: Association for Computing Machinery (ACM)

Date: 31-05-2008

DOI: 10.1145/1412734.1412736

Abstract: The value of knowledge obtainable by analysing large quantities of data is widely acknowledged. However, so-called primary or raw data may not always be available for knowledge discovery for several reasons. First, cooperating institutions that are interested in sharing knowledge may not be willing (or allowed) to disclose their primary data. Second, data in the form of streams are only temporarily available for processing. If stored at all, stream data are maintained in the form of synopses or derived, abstract representations of the original data. Finally, even for non-stream data, there are limits on the computation speed to be achieved -- such limits are set by hardware and firmware technologies. This problem can only be partially solved through parallelization and increased processing power. Ultimately, in many cases data must be summarized to be processed efficiently. In the light of these observations, we anticipate the need for defining and practising data mining without the luxury of primary data. To that end, we formally introduce the paradigm of Higher Order Mining as a form of data mining that is applied over non-primary, derived data or patterns. Although Higher Order Mining is a new paradigm, there are already research advances on knowledge discovery methods from patterns rather than data. We discuss them and organize them under the light of the new paradigm. We show that the HOM paradigm reveals further potential for knowledge discovery, including the delivery of rules and patterns with semantics that are closer to human intuition and are thus more appropriate for human inspection.

Journal on Data Semantics XI

Publisher: Springer Berlin Heidelberg

Date: 2008

DOI: 10.1007/978-3-540-92148-6

Imperial College London

Location: United Kingdom of Great Britain and Northern Ireland

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Flinders University

Location: Australia

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La Trobe University

Location: Australia

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Deakin University - Geelong Campus At Waurn Ponds

Location: Australia

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Enhancement Of South Australian High-performance Computing Facilities

Start Date: 2011

End Date: 2012

Funder: Australian Research Council

Behavioural Syndromes And Social Networks In Sleepy Lizards

Start Date: 2010

End Date: 2012

Funder: Australian Research Council

AusStage: Australian Performing Arts Gateway, Phase Two - Enhancement And Information Retrieval

Start Date: 2003

End Date: 2003

Funder: Australian Research Council

Improving Australia's Data Mining And Knowledge Discovery Research

Start Date: 2003

End Date: 2003

Funder: Australian Research Council

Perturbations In Complex Systems And Games

Start Date: 2015

End Date: 2017

Funder: Australian Research Council

Techniques For Active Conceptual Modelling And Guided Data Mining For Rapid Knowledge Discovery

Start Date: 2011

End Date: 2013

Funder: Australian Research Council

Strategic Business Principles For The Economic Development Of New Technologies In Regional Areas

Start Date: 2004

End Date: 2006

Funder: Australian Research Council

Addressing Indigenous Complex Health, Housing And Social Inclusion Issues Through Critical Systems Approaches To Build Workforce Capacity

Start Date: 2005

End Date: 2007

Funder: Australian Research Council

Managing And Mining Evolving Ontologies Through Combining Patterns Languages And Data Mining

Start Date: 2006

End Date: 2008

Funder: Australian Research Council

AusStage, Phase 5: Australian Live Performance And The World - Global Networks, National Culture, Aesthetic Transmission

Start Date: 2014

End Date: 2014

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0454353

Start Date: 02-2004

End Date: 12-2010

Amount: $73,168.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP150100618

Start Date: 01-2015

End Date: 12-2022

Amount: $379,700.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0667714

Start Date: 10-2006

End Date: 05-2011

Amount: $147,900.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP1094894

Start Date: 2010

End Date: 12-2012

Amount: $375,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP110100166

Start Date: 11-2011

End Date: 03-2015

Amount: $163,332.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0560406

Start Date: 04-2005

End Date: 04-2008

Amount: $76,444.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE110100234

Start Date: 07-2011

End Date: 07-2012

Amount: $430,000.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0354744

Start Date: 2004

End Date: 06-2006

Amount: $20,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0346553

Start Date: 2002

End Date: 12-2003

Amount: $280,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE140100024

Start Date: 06-2014

End Date: 12-2017

Amount: $325,000.00

Funder: Australian Research Council