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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46353

    Title: Adaptive Hyper-Fuzzy Partition Particle Swarm Optimization Clustering Algorithm
    Authors: Feng, Hsuan-ming;Chen, Ching-yi;余繁;Ye, Fun
    Contributors: 淡江大學電機工程學系
    Date: 2006-08-01
    Issue Date: 2010-08-10 10:59:26 (UTC+8)
    Publisher: Taylor & Francis
    Abstract: 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.
    Relation: Cybernetics and Systems 37(5), pp.463-479
    DOI: 10.1080/01969720600683429
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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