"This book provides a systematic collection on the post-mining, summarization and presentation of association rule, as well as new forms of association rules"--Provided by publisher.
Association rule mining is used in fields such as retail, insurance, and bioinformatics. This work presents recent research on reducing the number of association rules after association mining exercises. Rather than presenting algorithms or models for mining association rules themselves, the book explains methods for extracting useful and actionable knowledge after discovering a large number of association rules. After an overview of association rule mining, material is in sections on identifying interesting rules using objective and subjective measures and user feedback, post-analysis and post-mining of association rules, rule selection for classification and representation of association rules, and methods for the maintenance of association rules. The book is aimed at researchers, advanced students, and practitioners in the field of data mining. Zhao is affiliated with the University of Technology, Australia. Annotation ©2009 Book News, Inc., Portland, OR (booknews.com)
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| Preface |
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xvi | |
| Acknowledgement |
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Association Rules: An Overview |
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1 | (11) |
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Section II Identifying Interesting Rules |
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From Change Mining to Relevance Feedback: A Unified View on Assessing Rule Interestingness |
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12 | (26) |
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Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules |
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38 | (18) |
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Veronica Oliveira de Carvalho |
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Semantics-Based Classification of Rule Interestingness Measures |
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56 | (25) |
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Section III Post-Analysis and Post-Mining of Association Rules |
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Post-Processing for Rule Reduction Using Closed Set |
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81 | (19) |
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A Conformity Measure Using Background Knowledge for Association Rules Application to Text Mining |
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100 | (16) |
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Continuous Post-Mining of Association Rules in a Data Stream Management System |
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116 | (17) |
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QROC: A Variation of ROC Space to Analyze Item Set Costs/Benefits in Association Rules |
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133 | (17) |
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Section IV Rule Selection for Classification |
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Variations on Associative Classifiers and Classification Results Analyses |
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150 | (23) |
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Selection of High Quality Rules in Associative Classification |
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173 | (27) |
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Section V Visualization and Representation of Association Rules |
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Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules |
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200 | (24) |
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Visualization to Assist the Generation and Exploration of Association Rules |
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224 | (22) |
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Maria Cristina Ferreira de Oliveira |
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Frequent Closed Itemsets Based Condensed Representations for Association Rules |
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246 | (27) |
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Section VI Maintenance of Association Rules and New Forms of Association Rules |
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Maintenance of Frequent Patterns: A Survey |
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273 | (21) |
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Mining Conditional Contrast Patterns |
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294 | (17) |
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Multidimensional Model-Based Decision Rules Mining |
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311 | (24) |
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| Compilation of References |
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335 | (26) |
| About the Contributors |
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361 | (9) |
| Index |
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370 | |
Yanchang Zhao is a Postdoctoral Research Fellow in Data Sciences & Knowledge Discovery Research Lab, Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering & IT, University of Technology, Sydney, Australia. His research interests focus on association rules, sequential patterns, clustering and post-mining. He has published more than 30 papers on the above topics, including six journal articles and two book chapters. He served as a chair of two international workshops, and a program committee member for 11 international conferences and a reviewer for 8 international journals and over a dozen of international conferences. Chengqi Zhang is a Research Professor in Faculty of Engineering & IT, University of Technology, Sydney, Australia. He is the director of the Director of UTS Research Centre for Quantum Computation and Intelligent Systems and a Chief Investigator in Data Mining Program for Australian Capital Markets on Cooperative Research Centre. He has been a chief investigator of eight research projects. His research interests include Data Mining and Multi-Agent Systems. He is a co-author of three monographs, a co-editor of nine books, and an author or co-author of more than 150 research papers. He is the chair of the ACS (Australian Computer Society) National Committee for Artificial Intelligence and Expert Systems, a chair/member of the Steering Committee for three international conference. Longbing Cao is an Associate Professor in Faculty of Engineering & IT, University of Technology, Sydney (Australia). He is the Director of Data Sciences & Knowledge Discovery Research Lab. His research interest focuses on domain driven data mining, multi-agents, and the integration of agent and data mining. He is a chief investigator of two ARC (Australian Research Council) Discovery projects and one ARC Linkage project. He has over 50 publications, including one monograph, two edited books and 10 journal articles. He is a program co-chair of 11 international conferences.
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