Finding Pareto-front Membership Functions in Fuzzy Data Mining

doi:10.1080/18756891.2012.685314

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/92506

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题名:	Finding Pareto-front Membership Functions in Fuzzy Data Mining
作者:	Chen, Chun-Hao;Hong, Tzung-Pei;Tseng, Vincent S.
贡献者:	淡江大學資訊工程學系
关键词:	multi-objective optimization;genetic algorithm;fuzzy set;fuzzy association rules;data mining;Pareto front
日期:	2012-04
上传时间:	2013-10-16 16:17:42 (UTC+8)
出版者:	Paris: Atlantis Press
摘要:	Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases. In fuzzy data mining, the membership functions have a critical influence on the final mining results. How to effectively decide the membership functions in fuzzy data mining thus becomes very important. In the past, we proposed a fuzzy mining approach based on the Multi-Objective Genetic Algorithm (MOGA) to find the Pareto front of the desired membership functions. In this paper, we adopt a more sophisticated multi-objective approach, the SPEA2, to find the appropriate sets of membership functions for fuzzy data mining. Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions and the second one is the total number of large 1-itemsets derived. Experimental comparisons of the proposed and the previous approaches are also made to show the effectiveness of the proposed approach in finding the Pareto-front membership functions.
關聯:	International Journal of Computational Intelligence Systems 5(2), pp.343-354
DOI:	10.1080/18756891.2012.685314
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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