数据流中离群和变化发现研究

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1、j710‘0656分类号：婴31密级：公珏UDC：——学号：Q2窆12璺东南大掌博士学位论文数据流中离群和变化发现研究研究生姓名：扬宣塞导师姓名：处麦握教授申请学位级别王堂燧±论文提交日期窒QQ曼生!!县至!旦学位授予单位峦直去堂学科专业名称j±簋扭廑围论文答辩日期2Q垒且旦学位授予日期．至Q．生且且答辩委员会主席——评阅人2005年11月21日AbstractDataminingisnon-trivi越proce∞thatextractshidden，u丑】Ⅲown，potentiallyusefulknowledgeandpatternfromhugevolumeofdata．

2、Theseyears，manyscholarshavedonedeepandcomprehensiveresearchonthistopic．Recently,withmoreandmoreapplicationsofcomputertechniquesinmanyfields，thereoCcurSanewdatamodel，namelydatastream，whichchaUengescurrentdataminingalgorithms．Themodelofdatastreamexistsinmany鑫el蕊such龉矗珏an∞applications，networkmoni

3、toring，communicationdatamanagement，Webap斑ication，sensornetwork，etc，Begausedatastreamhasitsownclmr毡ctars。such8spotentiallyinfinite．arrivingonlineandrapidly,traditionalminingalgorithmsc8丑notmeetrequestsofthisnewmodel．Dataminingondatast#eamisbecomingahotresearchissue．0utlierdetectionandchangedete

4、ctionarebothimportantdataminingmissionsbe-eanseoftheirfunctionsinknowledgediscovery．ButcurrentalgorithmsforoutHerandchangedetectionaredesignedfortraditionalstaticdatasets。andc∞notbeappliedfordatastream。Tosolvetheproblemof赫馥timeandspacecomplexityofcurrentoutlierdetectionmethod，thisthesispropose

5、safastoutlierdetectionalgorithmfordatastreamsbasedondynamicgrids．whichtakesTavnstfledatastream瞄researchobject．Basedondynamicallygridpartitioningdataspace，thismethodcanfilteroutmostofnormaldataactordingtothedjstributionofdataandapproximationcomputation．whichreducesthesizeofcandidateoutliersandi

6、mprovestheefficiencyofdetectiongreatly．Resultsofexperimentsonsyntheticandrealdatasetsshowpromisingavailabilitiesofoarapproaches．锄1isthesisalsodiscussesdetailsof掣避partitioning，storing，filteringandoutliercomputatiomTimeandspacecomplexityofthisalgorithmarean越yzed，Resultsofexperimentsonsynthetican

7、drealdatasetsshowpromisingavailabflitiesofourapproaches．Therearemanydistributeddatastreamsdatainallkindsofapplications．Thisthesisextendstheproblemofoutlierdetectionindatastreamtothedistributedenvironmentandintroducesanalgorithmforoutlie