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1、IEEETRANSACTIONSONNEURALNETWORKS,VOL.16,NO.3,MAY2005645SurveyofClusteringAlgorithmsRuiXu,StudentMember,IEEEandDonaldWunschII,Fellow,IEEEAbstract—Dataanalysisplaysanindispensableroleforun-[60],[167].Whentheinducerreachesconvergenceortermi-derstandingvariousphenomena.Clusteranalysis,primit
2、ivenates,aninducedclassifierisgenerated[167].explorationwithlittleornopriorknowledge,consistsofresearchInunsupervisedclassification,calledclusteringorex-developedacrossawidevarietyofcommunities.Thediversity,ploratorydataanalysis,nolabeleddataareavailable[88],ononehand,equipsuswithmanytools
3、.Ontheotherhand,theprofusionofoptionscausesconfusion.Wesurveyclustering[150].Thegoalofclusteringistoseparateafiniteunlabeledalgorithmsfordatasetsappearinginstatistics,computerscience,datasetintoafiniteanddiscretesetof“natural,”hiddendataandmachinelearning,andillustratetheirapplicationsinso
4、mestructures,ratherthanprovideanaccuratecharacterizationbenchmarkdatasets,thetravelingsalesmanproblem,andbioin-ofunobservedsamplesgeneratedfromthesameprobabilityformatics,anewfieldattractingintensiveefforts.Severaltightlydistribution[23],[60].Thiscanmakethetaskofclusteringfallrelatedtopic
5、s,proximitymeasure,andclustervalidation,arealsodiscussed.outsideoftheframeworkofunsupervisedpredictivelearningproblems,suchasvectorquantization[60](seeSectionII-C),IndexTerms—Adaptiveresonancetheory(ART),clustering,probabilitydensityfunctionestimation[38](seeSectionII-D),clusteringalgori
6、thm,clustervalidation,neuralnetworks,prox-imity,self-organizingfeaturemap(SOFM).[60],andentropymaximization[99].Itisnoteworthythatclusteringdiffersfrommultidimensionalscaling(perceptualmaps),whosegoalistodepictalltheevaluatedobjectsinaI.INTRODUCTIONwaythatminimizesthetopographicaldistort
7、ionwhileusingasEARElivinginaworldfullofdata.Everyday,peoplefewdimensionsaspossible.Alsonotethat,inpractice,manyWencounteralargeamountofinformationandstoreor(predictive)vectorquantizersarealsousedfor(nonpredictive)representitasdata,forfurtheranalysisandmanagement.Onecluste
