Mining Time-Changing Data Streams

dc.contributor.authorTao, Yingying
dc.date.accessioned2011-10-28T15:28:05Z
dc.date.available2011-10-28T15:28:05Z
dc.date.issued2011-10-28T15:28:05Z
dc.date.submitted2011
dc.description.abstractStreaming data have gained considerable attention in database and data mining communities because of the emergence of a class of applications, such as financial marketing, sensor networks, internet IP monitoring, and telecommunications that produce these data. Data streams have some unique characteristics that are not exhibited by traditional data: unbounded, fast-arriving, and time-changing. Traditional data mining techniques that make multiple passes over data or that ignore distribution changes are not applicable to dynamic data streams. Mining data streams has been an active research area to address requirements of the streaming applications. This thesis focuses on developing techniques for distribution change detection and mining time-changing data streams. Two techniques are proposed that can detect distribution changes in generic data streams. One approach for tackling one of the most popular stream mining tasks, frequent itemsets mining, is also presented in this thesis. All the proposed techniques are implemented and empirically studied. Experimental results show that the proposed techniques can achieve promising performance for detecting changes and mining dynamic data streams.en
dc.identifier.urihttp://hdl.handle.net/10012/6374
dc.language.isoenen
dc.pendingfalseen
dc.publisherUniversity of Waterlooen
dc.subjectdata streamen
dc.subjectdistribution changeen
dc.subject.programComputer Scienceen
dc.titleMining Time-Changing Data Streamsen
dc.typeDoctoral Thesisen
uws-etd.degreeDoctor of Philosophyen
uws-etd.degree.departmentSchool of Computer Scienceen
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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