Post-mining of association rules: Techniques for effective knowledge extraction

Zhao, Y; Zhang, C; Cao, L

Post-mining of association rules: Techniques for effective knowledge extraction

Zhao, Y Zhang, C Cao, L

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Publication Type:: Chapter
Citation:: 2009, pp. 1 - 372
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Zhang, C	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	2009, pp. 1 - 372	en_US
dc.identifier.isbn	9781605664040	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12438
dc.description.abstract	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. © 2009 by IGI Global. All rights reserved.	en_US
dc.relation.isbasedon	10.4018/978-1-60566-404-0	en_US
dc.title	Post-mining of association rules: Techniques for effective knowledge extraction	en_US
dc.type	Chapter
utslib.for	120402 Engineering Design Knowledge	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

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. © 2009 by IGI Global. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/12438