Data Clustering

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dc.contributor.author Zhao, Yanchang en_US
dc.contributor.author Cao, Longbing en_US
dc.contributor.author Zhang, Huaifeng en_US
dc.contributor.author Zhang, Chengqi en_US
dc.contributor.editor Viviana E. Ferraggine, Jorge Horacio Doorn, Laura C. Rivero en_US
dc.date.accessioned 2010-07-13T08:46:17Z
dc.date.available 2010-07-13T08:46:17Z
dc.date.issued 2009 en_US
dc.identifier 2009005758 en_US
dc.identifier.citation Zhao Yanchang et al. 2009, 'Data Clustering', in http://dx.doi.org/10.4018/978-1-60566-242-8 (ed.), IGI Global, USA, pp. 562-572. en_US
dc.identifier.issn 9781605662428 en_US
dc.identifier.other B1 en_US
dc.identifier.uri http://hdl.handle.net/10453/12410
dc.description.abstract Clustering is one of the most important techniques in data mining. This chapter presents a survey of popular approaches for data clustering, including well-known clustering techniques, such as partitioning clustering, hierarchical clustering, density-based clustering and grid-based clustering, and recent advances in clustering, such as subspace clustering, text clustering and data stream clustering. The major challenges and future trends of data clustering will also be introduced in this chapter. The remainder of this chapter is organized as follows. The background of data clustering will be introduced in Section 2, including the definition of clustering, categories of clustering techniques, features of good clustering algorithms, and the validation of clustering. Section 3 will present main approaches for clustering, which range from the classic partitioning and hierarchical clustering to recent approaches of bi-clustering and semisupervised clustering. Challenges and future trends will be discussed in Section 4, followed by the conclusions in the last section. en_US
dc.language en_US
dc.publisher IGI Global en_US
dc.relation.isbasedon http://dx.doi.org/10.4018/978-1-60566-242-8 en_US
dc.title Data Clustering en_US
dc.parent Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Tr en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 562 en_US
dc.identifier.endpage 572 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.for 080604 en_US
dc.personcode 998488 en_US
dc.personcode 034535 en_US
dc.personcode 995032 en_US
dc.personcode 011221 en_US
dc.percentage 100 en_US
dc.classification.name Database Management en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.staffid 011221 en_US


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