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1、AbstractThedevelopmentofWeb2．0technologymakestheeraofbigdatacoming．WiththerapiddevelopmentonsocialnetworksuchaSmicro-blog,itbringsSOmanychallengesondataminingandknowledgediscovery，althoughthemicro—blogenrichesthebigdata．Comparedwiththetraditionaltextdata，themicro—blogd
2、atahaSsomedifferenceamongthepersonalinterest，entertainment，businessmarketing，andthepublicpublicity，etc．Furthermore，themicro-blogdataalsohasitsownpropertiesbothoncontentfragmentationanditsmassdata．Howtoanalyzeandmineitshidinginformationisanimportantresearchtask．Topicclu
3、steringisabasicworkonmicro—blogresearch．Byclusteringrelevantmassdataintoseveralgroupsautomatically，itsprocessingresultscanpresentsomehintsonanalyzingandminingthedata．AsthetraditionalapproachesusuallypresentSOmanyresultswithirrelevantorreplicatedinformation，itisnotfeasi
4、bletoprocesstheaboveproblemseffectively．ThetopicclusteringapproachCangrouptherelevantinformationautomatically．Furthermore，byusingthekeywordextraction，theprocessingvisualresultsareintuitional．Thisthesisdosomeresearchworksbasedonmicro—blogbyusingsomeintelligentalgorithms
5、，andthemainworksareasfollows：Firstly,wepresentsomeusefulapproachesonobtainingmicro—blogstructureddataanddatapre’processingbeforeclustering．Secondly，onthebasisofanalyzingthemicro—blogdata，wedoresearchonselectingusefulfeaturesforfurtherprocessing．Thirdly，wedesignalleffec
6、tivelyclusteringalgorithm．Onanalyzingthemicro-blogdata,wedoresearchonanalyzingwhichperformanceisbetter．Fourthly，weextractkeywordsfromclusteringresultset．ThesekeywordsCanbeusedtovisualthetopicclustering．Fifthly,wedothevisualizationprocessing，andtheresultsisclearandvisua
7、l，SOitisusefulonunderstandingandrecognitionthehiddeninformationbehindthemassdata．Theexperimentalresultsandanalysisshowsthefeasibleoftheproposedapproach．Someexistingproblemsandfurtherworksarealsopresentintheend．KeyWordsTopicclustering；Micro—blog；Featurevector；Visualizat
8、ion；InformationgainIII河北科技大學(xué)碩士學(xué)位論文IV目錄摘要?????????????????????????????????IAbstract?·-???????????????????·??????????一I
