Mining fuzzy association rules from uncertain data

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题名:	Mining fuzzy association rules from uncertain data
作者:	Weng,CH;Chen,YL
贡献者:	資訊管理學系
关键词:	MEMBERSHIP FUNCTIONS;SEQUENTIAL PATTERNS;GENETIC ALGORITHMS;FREQUENT PATTERNS;DATABASES;SUPPORT;CLASSIFICATION;SIMILARITY;INDUCTION;KNOWLEDGE
日期:	2010
上传时间:	2012-03-27 19:06:51 (UTC+8)
出版者:	國立中央大學
摘要:	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.
關聯:	KNOWLEDGE AND INFORMATION SYSTEMS
显示于类别:	[資訊管理學系] 期刊論文

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