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:Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering
學年
學期
出版(發表)日期2011/12/01
作品名稱Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering
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Heidelberg: Springer
著錄名稱、卷期、頁數Soft Computing 15(12), pp.2319-2333
摘要Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the previous approaches set a single minimum support threshold for all the items and identify the relationships among transactions using binary values. In real applications, different items may have different criteria to judge their importance. In the past, we proposed an algorithm for extracting appropriate multiple minimum support values, membership functions and fuzzy association rules from quantitative transactions. It used requirement satisfaction and suitability of membership functions to evaluate fitness values of chromosomes. The calculation for requirement satisfaction might take a lot of time, especially when the database to be scanned could not be totally fed into main memory. In this paper, an enhanced approach, called the fuzzy cluster-based genetic-fuzzy mining approach for items with multiple minimum supports (FCGFMMS), is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It divides the chromosomes in a population into several clusters by the fuzzy k-means clustering approach and evaluates each individual according to both their cluster and their own information. Experimental results also show the effectiveness and the efficiency of the proposed approach.
關鍵字Data mining;Fuzzy set;Genetic algorithm;Genetic-fuzzy mining;Fuzzy k-means;Clustering;Multiple minimum supports
語言英文
ISSN1432-7643; 1433-7479
期刊性質國外
收錄於SCI;
產學合作
通訊作者Hong, Tzung-Pei
審稿制度
國別德國
公開徵稿
出版型式,電子版,紙本
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