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1、Chapter5OverviewonTechniquesinClusterAnalysisItziarFradesandRuneMatthiesenAbstractClusteringistheunsupervised,semisupervised,andsupervisedclassificationofpatternsintogroups.Theclusteringproblemhasbeenaddressedinmanycontextsanddisciplines.Clusteranalysisencompassesdifferentmethodsandalgorithmsforg
2、roupingobjectsofsimilarkindsintorespectivecategories.Inthischapter,wedescribeanumberofmethodsandalgorithmsforclusteranalysisinastepwiseframework.Thestepsofatypicalclusteringanalysisprocessincludesequentiallypatternrepresentation,thechoiceofthesimilaritymeasure,thechoiceoftheclusteringalgorithm,t
3、heassessmentoftheoutput,andtherepresentationoftheclusters.Keywords:Clusteringalgorithm,featureselection,featureextraction,similaritymeasure,clustertendency,clustervalidity,clusterstability,relevancenetworks,dendrogram.1.Introduction1.1.TheImportanceClusteringisoneofthemostusefultasksinthedatamin
4、ingpro-ofClusteringcessfordiscoveringgroupsandidentifyingnewinterestingpat-ternsintheunderlyingdata.Clusteringalgorithmspartitiondataobjectsintosubsets(clusters)basedonsimilarityordissimilar-ity.Patternswithinavalidclusteraremoresimilartoeachotherthantheyaretoapatternbelongingtoadifferentcluster
5、.Theclusteringprocessisanunsupervised,semisupervised,orsuper-visedmethod.Sinceunsupervisedclusteralgorithmsdonotusepredefinedclasslabelsorexamplesthatwouldindicategroupingpropertiesinthedataset,itistheidealmethodforidentifyingnewpatternsindata.Unsupervisedclusteringisalsofrequentlyusedincombinati
6、onwithothersupervisedclassificationalgorithmssinceithasthepotentialtodetectincorrectclasslabels,outliers,errors,bias,andbadexperimentaldesigns.R.Matthiesen(ed.),BioinformaticsMethodsinClinicalResearch,MethodsinMolecularBiology593,DOI10.1007/978-1-60327-194-35,©HumanaPress,apartofSpringerScience+B
7、usinessMedia,LLC20108182FradesandMatthiesen(15)(15)Relevancenetworks(9)Displayingtheassessmentoftheuncertaintyinhier-archicalclusteranalysis(27)RepresentationofclustersGraphsPartitionsClassificationtreesDendrogram(7,8)Externa
