Mining fuzzy association rules from questionnaire data

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > journal & Dissertation > Item 987654321/29665

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29665

Title:	Mining fuzzy association rules from questionnaire data
Authors:	Chen,YL;Weng,CH
Contributors:	資訊管理研究所
Keywords:	ALGORITHM
Date:	2009
Issue Date:	2010-06-29 20:37:42 (UTC+8)
Publisher:	中央大學
Abstract:	Association rule mining is one of most popular data analysis methods that can discover associations within data, Association rule mining algorithms have been applied to various datasets, due to their practical usefulness. Little attention has been paid, however, on how to apply the association mining techniques to analyze questionnaire data. Therefore, this paper first identifies the various data types that may appear in a questionnaire. Then, we introduce the questionnaire data mining problem and define the rule patterns that can be mined from questionnaire data. A unified approach is developed based on fuzzy techniques so that all different data types can be handled in a uniform manner. After that, an algorithm is developed to discover fuzzy association rules from the questionnaire dataset. Finally, we evaluate the performance of the proposed algorithm, and the results indicate that our method is capable of finding interesting association rules that would have never been found by previous mining algorithms. (C) 2008 Elsevier B.V. All rights reserved.
Relation:	KNOWLEDGE-BASED SYSTEMS
Appears in Collections:	[Graduate Institute of Information Management] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	563	View/Open

社群 sharing