Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering
學年 100
學期 1
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 en
ISSN 1432-7643; 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 否
國別 DEU
公開徵稿
出版型式 ,電子版,紙本
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