CLUSTERING VIA DIMENSIONAL REDUCTION METHOD FOR THE PROJECTION PURSUIT BASED ON THE ICSA

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    摘要 Theperformanceoftheclassicalclusteringalgorithmisnotalwayssatisfiedwiththehigh-dimensionaldatasets,whichmakeclusteringmethodlimitedinmanyapplication.Tosolvethisproblem,clusteringmethodwithProjectionPursuitdimensionreductionbasedonImmuneClonalSelectionAlgorithm(ICSA-PP)isproposedinthispaper.ProjectionpursuitstrategycanmaintainconsistentEuclideandistancesbetweenpointsinthelow-dimensionalembeddingswheretheICSAisusedtosearchoptimizingprojectiondirection.Theproposedalgorithmcanconvergequicklywithlessiterationtoreducedimensionofsomehigh-dimensionaldatasets,andinwhichspace,K-meanclusteringalgorithmisusedtopartitionthereduceddata.TheexperimentresultsonUCIdatashowthatthepresentedmethodcansearchquickertooptimizeprojectiondirectionthanGeneticAlgorithm(GA)andithasbetterclusteringresultscomparedwithtraditionallineardimensionreductionmethodforPrincipleComponentAnalysis(PCA).
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    出版日期 2010年04月14日(中国Betway体育网页登陆平台首次上网日期,不代表论文的发表时间)
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