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1、Ann.Data.Sci.DOI10.1007/s40745-015-0040-1AComprehensiveSurveyofClusteringAlgorithmsDongkuanXu1,2·YingjieTian2,3Received:25May2015/Revised:18July2015/Accepted:31July2015©Springer-VerlagBerlinHeidelberg2015AbstractDataanalysisisusedasacommonmethodinmodernscienceresearch,whichisacrossc
2、ommunicationscience,computerscienceandbiologyscience.Clus-tering,asthebasiccompositionofdataanalysis,playsasignificantrole.Ononehand,manytoolsforclusteranalysishavebeencreated,alongwiththeinformationincreaseandsubjectintersection.Ontheotherhand,eachclusteringalgorithmhasitsownstrengt
3、hsandweaknesses,duetothecomplexityofinformation.Inthisreviewpaper,webeginatthedefinitionofclustering,takethebasicelementsinvolvedinthecluster-ingprocess,suchasthedistanceorsimilaritymeasurementandevaluationindicators,intoconsideration,andanalyzetheclusteringalgorithmsfromtwoperspecti
4、ves,thetraditionalonesandthemodernones.AllthediscussedclusteringalgorithmswillbecomparedindetailandcomprehensivelyshowninAppendixTable22.KeywordsClustering·Clusteringalgorithm·Clusteringanalysis·Survey·UnsupervisedlearningBYingjieTiantyj@ucas.ac.cnDongkuanXuxudongkuan14@mails.ucas.a
5、c.cn1SchoolofMathematicalSciences,UniversityofChineseAcademyofSciences,Beijing100049,China2ResearchCenteronFictitiousEconomy&DataScience,ChineseAcademyofSciences,Beijing100190,China3KeyLaboratoryofBigDataMiningandKnowledgeManagement,ChineseAcademyofSciences,Beijing100190,China123Ann
6、.Data.Sci.1IntroductionClustering,consideredasthemostimportantquestionofunsupervisedlearning,dealswiththedatastructurepartitioninunknownareaandisthebasisforfurtherlearning.Thecompletedefinitionforclustering,however,isn’tcometoanagreement,andaclassiconeisdescribedasfollows[1]:(1)Insta
7、nces,inthesamecluster,mustbesimilarasmuchaspossible;(2)Instances,inthedifferentclusters,mustbedifferentasmuchaspossible;(3)Measurementforsimilarityanddissimilaritymustbeclearandhavethepracticalmeaning;Thestandardprocessofclusteringcanbedividedintothefollowingseveralsteps[2]:(1)Featu
8、reextractionandsele
