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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/45264

    Title: An Improved Unsupervised Clustering Algorithm based on Population Markov Chain
    Authors: Yang, Fu-Wen;Lin, Hwei-Jen;Yen, Shwu-Huey
    Contributors: 淡江大學資訊工程學系
    Keywords: Unsupervised clustering;genetic algorithms;population Markov chain;cluster validity;Davies-Bouldin index
    Date: 2007
    Issue Date: 2010-03-26 18:53:51 (UTC+8)
    Publisher: Calgary: ACTA Press
    Abstract: 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]
    Relation: International Journal of Computers and Applications 29(3), pp.253-258
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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