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    題名: Alternative KPSO-Clustering Algorithm
    作者: 余繁;Ye, Fun;Chen, Ching-yi
    貢獻者: 淡江大學電機工程學系
    關鍵詞: Clustering;Particle Swarm Optimization;K-means
    日期: 2005-06
    上傳時間: 2010-03-26 21:01:28 (UTC+8)
    出版者: 淡江大學
    摘要: This paper presents an evolutionary particle swarm optimization (PSO) learning-based method to optimally cluster N data points into K clusters. The hybrid PSO and K-means algorithm with a novel alternative metric, called Alternative KPSO-clustering (AKPSO), is developed to automatically detect the cluster centers of geometrical structure data sets. The alternative metric is known has more robust ability than the common-used Euclidean norm. In AKPSO algorithm, the special alternative metric is considered to improve the traditional K-means clustering algorithm to deal with various structure data sets. For testing the performance of the proposed method, this paper will show the experience results by using several artificial and real data sets. Simulation results compared with some well-known clustering methods demonstrate the robustness and efficiency of the novel AKPSO method.
    關聯: 淡江理工學刊=Tamkang journal of science and engineering 8(2), pp.165-174
    DOI: 10.6180/jase.2005.8.2.09
    顯示於類別:[電機工程學系暨研究所] 期刊論文

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