Mining fuzzy association rules from uncertain data

NCUIR > School of Management at National Central University > Department of Information Management > journal & Dissertation > Item 987654321/51817

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51817

Title:	Mining fuzzy association rules from uncertain data
Authors:	Weng,CH;Chen,YL
Contributors:	資訊管理學系
Keywords:	MEMBERSHIP FUNCTIONS;SEQUENTIAL PATTERNS;GENETIC ALGORITHMS;FREQUENT PATTERNS;DATABASES;SUPPORT;CLASSIFICATION;SIMILARITY;INDUCTION;KNOWLEDGE
Date:	2010
Issue Date:	2012-03-27 19:06:51 (UTC+8)
Publisher:	國立中央大學
Abstract:	Association rule mining is an important data analysis method that can discover associations within data. There are numerous previous studies that focus on finding fuzzy association rules from precise and certain data. Unfortunately, real-world data tends to be uncertain due to human errors, instrument errors, recording errors, and so on. Therefore, a question arising immediately is how we can mine fuzzy association rules from uncertain data. To this end, this paper proposes a representation scheme to represent uncertain data. This representation is based on possibility distributions because the possibility theory establishes a close connection between the concepts of similarity and uncertainty, providing an excellent framework for handling uncertain data. Then, we develop an algorithm to mine fuzzy association rules from uncertain data represented by possibility distributions. Experimental results from the survey data show that the proposed approach can discover interesting and valuable patterns with high certainty.
Relation:	KNOWLEDGE AND INFORMATION SYSTEMS
Appears in Collections:	[Department of Information Management] journal & Dissertation

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