-------- Original-Nachricht -------- Betreff: [isworld] New book announcement: Post-Mining of Association Rules - Techniques for Effective Knowledge Extraction Datum: Sat, 13 Jun 2009 00:10:39 +0000 Von: Yanchang Zhao zhaoyanchang@hotmail.com Antwort an: Yanchang Zhao zhaoyanchang@hotmail.com An: AISWORLD Information Systems World Network isworld@lyris.isworld.org

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New book release

Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction

ISBN: 978-1-60566-404-0; 394 pp; May 2009

Edited by: Yanchang Zhao, Chengqi Zhang, and Longbing Cao

University of Technology, Sydney, Australia

Published under the imprint Information Science Reference

(formerly Idea Group Reference)

http://www.igi-global.com/reference/details.asp?ID=33406

http://www-staff.it.uts.edu.au/~yczhao/book-PMAR.htm

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DESCRIPTION

There is often a large number of association rules discovered

in data mining practice, making it difficult for users to

identify those that are of particular interest to them.

Therefore, it is important to remove insignificant rules and

prune redundancy as well as summarize, visualize, and

post-mine the discovered rules.

Post-Mining of Association Rules: Techniques for Effective

Knowledge Extraction provides a systematic collection on

post-mining, summarization and presentation of association

rules, and new forms of association rules. This book presents

researchers, practitioners, and academicians with tools to

extract useful and actionable knowledge after discovering

a large number of association rules.

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"This book examines the post-analysis and post-mining of

association rules to find useful knowledge from a large

number of discovered rules and presents a systematic view

of the above topic."

- Yanchang Zhao, University of Technology Sydney, Australia

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TOPICS COVERED

Association rules

Background knowledge for association

Classification results analyses

Data stream management system

Maintenance of association rules

Meta-knowledge based approach

New forms of association rules

Post-mining of association rules

Semantics-based classification

Variations on associative classifiers

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For more information about Post-Mining of Association Rules:

Techniques for Effective Knowledge Extraction, you can view

the title information sheet at

http://www.igi-global.com/downloads/pdf/33406.pdf.

To view the Table of Contents and a complete list of

contributors online go to

http://www.igi-global.com/reference/details.asp?ID=33406&v=tableOfConten....

You can also view the first chapter of the publication at

http://www.igi-global.com/downloads/excerpts/33406.pdf.

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ABOUT THE EDITORS

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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To view the full contents of this publication, check for

Post-Mining of Association Rules: Techniques for Effective

Knowledge Extraction in your institution’s library. If your

library does not currently own this title, please recommend

it to your librarian.

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