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DistributedComputingSeminarLecture4:Clustering–anOverviewandSampleMapReduceImplementationChristopheBisciglia,AaronKimball,&SierraMichels-SlettvetGoogle,Inc. Summer2007Exceptasotherwisenoted,thecontentofthispresentationislicensedundertheCreativeCommonsAttribution2.5License.清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第1頁(yè)!OutlineClusteringIntuitionClusteringAlgorithmsTheDistanceMeasureHierarchicalvs.PartitionalK-MeansClusteringComplexityCanopyClusteringMapReducingalargedatasetwithK-MeansandCanopyClustering清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第2頁(yè)!ClusteringWhatisclustering?清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第3頁(yè)!Otherlessglamorousthings...HospitalRecordsScientificImagingRelatedgenes,relatedstars,relatedsequencesMarketResearchSegmentingmarkets,productpositioningSocialNetworkAnalysisDataminingImagesegmentation…清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第4頁(yè)!HierarchicalClusteringvs.PartitionalClusteringTypesofAlgorithms清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第5頁(yè)!PartitionalClusteringPartitionssetintoallclusterssimultaneously.清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第6頁(yè)!K-MeansClusteringSupersimplePartitionalClusteringChoosethenumberofclusters,kChoosekpointstobeclustercentersThen…清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第7頁(yè)!But!Theplexityisprettyhigh:k*n*O(distancemetric)*num(iterations)
Moreover,itcanbenecessarytosendtonsofdatatoeachMapperNode.Dependingonyourbandwidthandmemoryavailable,thiscouldbeimpossible.清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第8頁(yè)!CanopyClusteringPreliminarysteptohelpparallelizeputation.ClustersdataintooverlappingCanopiesusingsupercheapdistancemetric.EfficientAccurate清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第9頁(yè)!Afterthecanopyclustering…Resumehierarchicalorpartitionalclusteringasusual.Treatobjectsinseparateclustersasbeingatinfinitedistances.清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第10頁(yè)!TheDistanceMetricTheCanopyMetric($)TheK-MeansMetric($$$)清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第11頁(yè)!DataMassageThisisn’tinteresting,butithastobedone.清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第12頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第13頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第14頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第15頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第16頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第17頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第18頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第19頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第20頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第21頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第22頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第23頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第24頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第25頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第26頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第27頁(yè)!ElbowCriterionChooseanumberofclusterss.t.addingaclusterdoesn’taddinterestinginformation.RuleofthumbtodeterminewhatnumberofClustersshouldbechosen.Initialassignmentofclusterseedshasbearingonfinalmodelperformance.Oftenrequiredtorunclusteringseveraltimestogetmaximalperformance清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第28頁(yè)!GoogleNewsTheydidn’tpickall3,400,217relatedarticlesbyhand…OrAmazon.OrNetflix…清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第29頁(yè)!TheDistanceMeasureHowthesimilarityoftwoelementsinasetisdetermined,e.g.EuclideanDistanceManhattanDistanceInnerProductSpaceMaximumNormOranymetricyoudefineoverthespace…清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第30頁(yè)!HierarchicalClusteringBuildsorbreaksupahierarchyofclusters.清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第31頁(yè)!PartitionalClusteringPartitionssetintoallclusterssimultaneously.清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第32頁(yè)!K-MeansClusteringiterate{Computedistancefromallpointstoallk-centersAssigneachpointtothenearestk-centerComputetheaverageofallpointsassignedto allspecifick-centersReplacethek-centerswiththenewaverages}清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第33頁(yè)!FurthermoreTherearethreebigwaysadatasetcanbelarge:Therearealargenumberofelementsintheset.Eachelementcanhavemanyfeatures.TherecanbemanyclusterstodiscoverConclusion–Clusteringcanbehuge,evenwhenyoudistributeit.清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第34頁(yè)!CanopyClusteringWhilethereareunmarkedpoints{pickapointwhichisnotstronglymarkedcallitacanopycenter markallpointswithinsomethresholdofitasinit’scanopy stronglymarkallpointswithinsomestrongerthreshold}清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第35頁(yè)!MapReduceImplementation:Problem–Efficientlypartitionalargedataset(say…movieswithuserratings!)intoafixednumberofclustersusingCanopyClustering,K-MeansClustering,andaEuclideandistancemeasure.清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第36頁(yè)!Steps!GetDataintoaformyoucanuse(MR)PickingCanopyCenters(MR)AssignDataPointstoCanopies(MR)PickK-MeansClusterCentersK-Meansalgorithm(MR)Iterate!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第37頁(yè)!SelectingCanopyCenters清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第38頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第39頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第40頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第41頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第42頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第43頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第44頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第45頁(yè)!AssigningPointstoCanopies清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第46頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第47頁(yè)!清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第48頁(yè)!K-MeansMap清華云計(jì)算課件分布式集群共54頁(yè),您現(xiàn)在瀏覽的是第
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