Data Clustering

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Show simple item record Zhao, Y Cao, L Zhang, H Zhang, C
dc.contributor.editor Ferraggine, VE
dc.contributor.editor Doorn, JH
dc.contributor.editor Rivero, LC 2010-07-13T08:46:17Z 2009-01
dc.identifier.citation Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Tr, 2009, 1, pp. 562 - 572
dc.identifier.isbn 9781605662428
dc.identifier.other B1 en_US
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.
dc.publisher IGI Global
dc.relation.isbasedon 10.4018/978-1-60566-242-8
dc.title Data Clustering
dc.type Chapter
dc.parent Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Tr
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 562 en_US
dc.identifier.endpage 572 en_US FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.for 080604 Database Management
dc.personcode 011221
dc.personcode 034535
dc.personcode 998488
dc.personcode 995032
dc.percentage 100 en_US Database Management en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords NA en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)

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