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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/60855

    Title: A New Cluster Validity Measure and Its Application to Image Compression
    Authors: Chou, C.H.;Su, M.C.;Lai, Eugene
    Contributors: 淡江大學電機工程學系
    Keywords: Clustering algorithm;Cluster validity;Image compression;Pattern recognition;Vector quantisation
    Date: 2004-11
    Issue Date: 2011-10-15 00:59:19 (UTC+8)
    Abstract: Many validity measures have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different densities and/or sizes. They usually have a tendency of ignoring clusters with low densities. In this paper, we propose a new validity measure that can deal with this situation. In addition, we also propose a modified K-means algorithm that can assign more cluster centres to areas with low densities of data than the conventional K-means algorithm does. First, several artificial data sets are used to test the performance of the proposed measure. Then the proposed measure and the modified K-means algorithm are applied to reduce the edge degradation in vector quantisation of image compression.
    Relation: Pattern Analysis and Applications 7, pp.205-220
    DOI: 10.1007/s10044-004-0218-1
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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