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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/5703


    Title: Robust clustervalidityindexes
    Authors: Kuo-Lung Wu (吳國龍)
    Miin-ShenYang
    June-NanHsieh
    Keywords: Cluster validityindex
    Clustering algorithms
    Fuzzy c-means
    Partition membership
    Mean
    Median
    Robust
    Noise
    Outlier
    Date: 2009-11
    Issue Date: 2009-11-20 00:27:09 (UTC+8)
    Abstract: Cluster validityindexescanbeusedtoevaluatethefitnessofdatapartitionsproducedbyaclustering
    algorithm. Validityindexesareusuallyindependentofclusteringalgorithms.However,thevaluesof
    validity indexesmaybeheavilyinfluencedbynoiseandoutliers.Thesenoiseandoutliersmaynot
    influence theresultsfromclusteringalgorithms,buttheymayaffectthevaluesofvalidityindexes.In
    the literature,thereislittlediscussionabouttherobustnessofclustervalidityindexes.Inthispaper,we
    analyze therobustnessofavalidityindexusingthe  function ofM-estimateandthenproposeseveral
    robust-type validityindexes.Firstly,wediscussthevaliditymeasureonasingledatapointandfocuson
    those validityindexesthatcanbecategorizedasthemeantypeofvalidityindexes.Wethenpropose
    median-type validityindexesthatarerobusttonoiseandoutliers.Comparativeexampleswithnumerical
    and realdatasetsshowthattheproposedmedian-typevalidityindexesworkbetterthanthemean-type
    validity indexes.
    Relation: K.L. Wu*, M.S. Yang and J.N. Hsieh, Robust cluster validity indexes, Pattern Recognition, 42(11), pp. 2541-2550, 2009.
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