教師資料查詢 | 類別: 期刊論文 | 教師: 陳俊豪 CHUN-HAO CHEN (瀏覽個人網頁)

標題:Finding Pareto-front Membership Functions in Fuzzy Data Mining
學年100
學期2
出版(發表)日期2012/04/01
作品名稱Finding Pareto-front Membership Functions in Fuzzy Data Mining
作品名稱(其他語言)
著者Chen, Chun-Hao; Hong, Tzung-Pei; Tseng, Vincent S.
單位淡江大學資訊工程學系
出版者Paris: Atlantis Press
著錄名稱、卷期、頁數International Journal of Computational Intelligence Systems 5(2), pp.343-354
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.
關鍵字multi-objective optimization; genetic algorithm; fuzzy set; fuzzy association rules; data mining; Pareto front
語言英文(美國)
ISSN1875-6891; 1875-6883
期刊性質國外
收錄於
產學合作
通訊作者Hong, Tzung-Pei
審稿制度
國別法國
公開徵稿
出版型式紙本;電子版
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