Common clustering algorithms

Lee, I., and Yang, J. (2009) Common clustering algorithms. In: Brown, Steven D., Tauler, Roma, and Walczak, Beata, (eds.) Comprehensive Chemometrics: chemical and biochemical data analysis. Elsevier, Oxford, UK, pp. 577-618.

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Abstract

This chapter surveys common clustering algorithms widely used in the data mining community in light of chemometrics. It starts with taxonomy of clustering algorithms, and discusses two common clustering approaches – partitioning clustering and hierarchical clustering – in detail. Several variants of these clustering methods are presented and their strengths and weaknesses are addressed. This chapter continues to overview hybrid clustering approaches combining partitioning clustering and hierarchical clustering, and concludes with a quick overview on constrained clustering.

Item ID: 8695
Item Type: Book Chapter (Reference)
Keywords: data mining; agglomerative clustering; clustering; constrained clustering; divisive clustering; hierarchical clustering; hybrid clustering; k-Means clustering; k-medoid clustering; partitioning clustering; unsupervised learning
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ISBN: 978-0-444-52702-8
Date Deposited: 02 Mar 2010 00:34
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 100%
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