Using decision trees to summarize associative classification rules

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > journal & Dissertation > Item 987654321/29674

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29674

Title:	Using decision trees to summarize associative classification rules
Authors:	Chen,YL;Hung,LTH
Contributors:	資訊管理研究所
Keywords:	CONSTRUCTION
Date:	2009
Issue Date:	2010-06-29 20:37:56 (UTC+8)
Publisher:	中央大學
Abstract:	Association rule mining is one of the most popular issues within data mining. It discovers items that co-occur frequently within a set of transactions and determines rules based oil these co-occurrence relations. Classification problems have adopted association rules for years (associative classification). Once the rules have been generated, however, their lack of organization causes a readability problem, meaning it is difficult for users to analyze them and obtain a good understanding of the domain. Therefore, our work presents two algorithms that use decision trees to summarize associative classification rules. The obtained classification model combines the advantages of associative classification and decision trees. it organizes knowledge in it more readable, compact, well-organized form and is easier to use than associative classification. It also provides better classification accuracy than the traditional decision tree algorithm. (C) 2007 Elsevier Ltd. All rights reserved.
Relation:	EXPERT SYSTEMS WITH APPLICATIONS
Appears in Collections:	[Graduate Institute of Information Management] journal & Dissertation

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