土木工程专业聚类分析基本概念及算法大学毕业论文外文文献翻译及原文Word格式.docx
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土木工程专业聚类分析基本概念及算法大学毕业论文外文文献翻译及原文Word格式.docx
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文献、资料中文题目:
聚类分析—基本概念及算法
文献、资料英文题目:
文献、资料来源:
文献、资料发表(出版)日期:
院(部):
专业:
土木工程
班级:
姓名:
学号:
指导教师:
翻译日期:
2017.02.14
本科毕业论文
外文文献及译文
文献、资料题目:
ClusterAnalysis
—BasicConceptsandAlgorithms
土木工程学院
土木工程
外文文献:
ClusterAnalysis
Clusteranalysisdividesdataintogroups(clusters)thataremeaningful,useful,orboth.Ifmeaningfulgroupsarethegoal,thentheclustersshouldcapturethenaturalstructureofthedata.Insomecases,however,clusteranalysisisonlyausefulstartingpointforotherpurposes,suchasdatasummarization.Whetherforunderstandingorutility,clusteranalysishaslongplayedanimportantroleinawidevarietyoffields:
psychologyandothersocialsciences,biology,statistics,patternrecognition,informationretrieval,machinelearning,anddatamining.
Therehavebeenmanyapplicationsofclusteranalysistopracticalproblems.Weprovidesomespecificexamples,organizedbywhetherthepurposeoftheclusteringisunderstandingorutility.
ClusteringforUnderstandingClasses,orconceptuallymeaningfulgroupsofobjectsthatsharecommoncharacteristics,playanimportantroleinhowpeopleanalyzeanddescribetheworld.Indeed,humanbeingsareskilledatdividingobjectsintogroups(clustering)andassigningparticularobjectstothesegroups(classification).Forexample,evenrelativelyyoungchildrencanquicklylabeltheobjectsinaphotographasbuildings,vehicles,people,animals,plants,etc.Inthecontextofunderstandingdata,clustersarepotentialclassesandclusteranalysisisthestudyoftechniquesforautomaticallyfindingclasses.Thefollowingaresomeexamples:
Biology.Biologistshavespentmanyyearscreatingataxonomy(hierarchicalclassification)ofalllivingthings:
kingdom,phylum,class,order,family,genus,andspecies.Thus,itisperhapsnotsurprisingthatmuchoftheearlyworkinclusteranalysissoughttocreateadisciplineofmathematicaltaxonomythatcouldautomaticallyfindsuchclassificationstructures.Morerecently,biologistshaveappliedclusteringtoanalyzethelargeamountsofgeneticinformationthatarenowavailable.Forexample,clusteringhasbeenusedtofindgroupsofgenesthathavesimilarfunctions.
•InformationRetrieval.TheWorldWideWebconsistsofbillionsofWebpages,andtheresultsofaquerytoasearchenginecanreturnthousandsofpages.Clusteringcanbeusedtogroupthesesearchresultsintoasmallnumberofclusters,eachofwhichcapturesaparticularaspectofthequery.Forinstance,aqueryof“movie”mightreturnWebpagesgroupedintocategoriessuchasreviews,trailers,stars,andtheaters.Eachcategory(cluster)canbebrokenintosubcategories(sub-clusters),producingahierarchicalstructurethatfurtherassistsauser’sexplorationofthequeryresults.
•Climate.UnderstandingtheEarth’sclimaterequiresfindingpatternsintheatmosphereandocean.Tothatend,clusteranalysishasbeenappliedtofindpatternsintheatmosphericpressureofpolarregionsandareasoftheoceanthathaveasignificantimpactonlandclimate.
•PsychologyandMedicine.Anillnessorconditionfrequentlyhasanumberofvariations,andclusteranalysiscanbeusedtoidentifythesedifferentsubcategories.Forexample,clusteringhasbeenusedtoidentifydifferenttypesofdepression.Clusteranalysiscanalsobeusedtodetectpatternsinthespatialortemporaldistributionofadisease.
•Business.Businessescollectlargeamountsofinformationoncurrentandpotentialcustomers.Clusteringcanbeusedtosegmentcustomersintoasmallnumberofgroupsforadditionalanalysisandmarketingactivities.
ClusteringforUtility:
Clusteranalysisprovidesanabstractionfromindividualdataobjectstotheclustersinwhichthosedataobjectsreside.Additionally,someclusteringtechniquescharacterizeeachclusterintermsofaclusterprototype;
i.e.,adataobjectthatisrepresentativeoftheotherobjectsinthecluster.Theseclusterprototypescanbeusedasthebasisforanumberofdataanalysisordataprocessingtechniques.Therefore,inthecontextofutility,clusteranalysisisthestudyoftechniquesforfindingthemostrepresentativeclusterprototypes.
•Summarization.Manydataanalysistechniques,suchasregressionorPCA,haveatimeorspacecomplexityofO(m2)orhigher(wheremisthenumberofobjects),andthus,arenotpracticalforlargedatasets.However,insteadofapplyingthealgorithmtotheentiredataset,itcanbeappliedtoareduceddatasetconsistingonlyofclusterprototypes.Dependingonthetypeofanalysis,thenumberofprototypes,andtheaccuracywithwhichtheprototypesrepresentthedata,theresultscanbecomparabletothosethatwouldhavebeenobta
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