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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/55827

    Title: Detection of Line-Symmetry Clusters
    Authors: Hsieh, Yi-Zeng;Su, Mu-Chun;Chou, Chien-Hsing;Wang, Pa-Chun
    Contributors: 淡江大學電機工程學系
    Keywords: Cluster analysis;Clustering algorithm;Symmetry;Distance measure
    Date: 2011-08
    Issue Date: 2011-08-28 16:54:39 (UTC+8)
    Publisher: Kumamoto: I C I C International
    Abstract: Many real-world and man-made objects are symmetry. Therefore, it is reasonable to assume that some kinds of symmetry may exist in data clusters. The most common type of symmetry is line symmetry. In this paper, we propose a line symmetry distance measure. Based on the proposed line symmetry distance, a modified version of the K-means algorithm can be used to partition data into clusters with different geometrical shapes. Several data sets are used to test the performance of the proposed modified version of the K-means algorithm incorporated with the line symmetry distance.
    Relation: International Journal of Innovative Computing, Information and Control 7(8), pp.5027-5043
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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