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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/18160

    Title: A Crossover-Imaged Clustering Algorithm with Bottom-Up Tree Architecture
    Authors: Chang, Chung-i;Lin, N.P.
    Contributors: 淡江大學軍訓室;淡江大學資訊工程學系
    Keywords: Bottom-up tree;Crossover image;Data Mining;Grid-based clustering;Significant Cell
    Date: 2008-10-18
    Issue Date: 2009-08-13 11:26:38 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers(IEEE)
    Abstract: The grid-based clustering algorithms are efficient with low computation time, but the size of the predefined grids and the threshold of the significant cells are seriously influenced their effects. The ADCC [1] and ACICA+ [2] are two new grid-based clustering algorithms. The ADCC algorithm uses axis-shifted strategy and cell clustering twice to reduce the influences of the size of the cells and inherits the advantage with the low time complexity. And the ACICA+ uses the crossover image of significant cells and just only one cell clustering. But the extension of original significant cell in one crossover image is not easy to find what else clusters it belongs to. The crossover-imaged clustering algorithm with bottom-up tree architecture, called CIC-BTA, is proposed to use bottom-up tree architecture to have the same results. The main idea of CIC-BTA algorithm is to use the bottom-up tree architecture to link the significant cells to be the pre-clusters and combine pre-clusters into one by using semi-significant cells The final set of clusters is the result.
    Relation: Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on (Volume:2 ), pp.327-331
    DOI: 10.1109/FSKD.2008.652
    Appears in Collections:[Office of Military Education and Training] Proceeding
    [Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

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