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    題名: Adaptive Hyper-Fuzzy Partition Particle Swarm Optimization Clustering Algorithm
    作者: Feng, Hsuan-ming;Chen, Ching-yi;余繁;Ye, Fun
    貢獻者: 淡江大學電機工程學系
    日期: 2006-08
    上傳時間: 2010-08-10 10:59:26 (UTC+8)
    出版者: Taylor & Francis
    摘要: This article presents an adaptive hyper-fuzzy partition particle swarm optimization clustering algorithm to optimally classify different geometrical structure data sets into correct groups. In this architecture, we use a novel hyper-fuzzy partition metric to improve the traditional common-used Euclidean norm metric clustering method. Since one fuzzy rule describes one pattern feature and implies the detection of one cluster center, it is encouraged to decrease the number of fuzzy rules with the hyper-fuzzy partition metric. According to the adaptive particle swarm optimization, it is very suitable to manage the clustering task for a complex, irregular, and high dimensional data set. To demonstrate the robustness of the proposed adaptive hyper-fuzzy partition particle swarm optimization clustering algorithms, various clustering simulations are experimentally compared with K -means and fuzzy c-means learning methods.
    關聯: Cybernetics and Systems 37(5), pp.463-479
    DOI: 10.1080/01969720600683429
    顯示於類別:[電機工程學系暨研究所] 期刊論文

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