An Improved Unsupervised Clustering Algorithm based on Population Markov Chain
學年 95
學期 1
出版（發表）日期 2007-01-01
作品名稱 An Improved Unsupervised Clustering Algorithm based on Population Markov Chain
作品名稱（其他語言）
著者 Yang, Fu-Wen; Lin, Hwei-Jen; Yen, Shwu-Huey
單位 淡江大學資訊工程學系
出版者 Calgary: ACTA Press
著錄名稱、卷期、頁數 International Journal of Computers and Applications 29(3), pp.253-258
摘要 GA-based clustering approaches have the advantage of automatically determining the optimal number of clusters. In a previous work, we proposed an efficient GA-based clustering algorithm, the PMCC method, and compared it with a representative GA-based clustering algorithm, the GCUK method, to prove its efficiency and effectiveness. In this paper we modify this PMCC method to obtain an improved version: the WPMCC method. This modification prevents premature convergence problem caused in the PMCC method while maintaining the advantage of the PMCC method. The experimental results show that the proposed algorithm not only solves the problem of premature convergence, thereby providing a more stable clustering performance in terms of number of clusters and clustering results, but it also improves the efficiency in terms of time. [PUBLICATION ABSTRACT]
關鍵字 Unsupervised clustering;genetic algorithms;population Markov chain;cluster validity;Davies-Bouldin index
語言 en
ISSN 1206-212X
期刊性質 國外
收錄於 EI
產學合作
通訊作者 Yang, Fu-Wen; Lin, Hwei-Jen
審稿制度 是
國別 CAN
公開徵稿
出版型式 紙本
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