Model-based clustering

McLachlan, G. J. (2009). Model-based clustering. In Steven D. Brown, Roma Tauler and Beata Walczak (Ed.), Comprehensive chemometrics: chemical and biochemical data analysis (pp. 655-681) Oxford, U.K.: Elsevier Science. doi:10.1016/B978-044452701-1.00068-5

Author McLachlan, G. J.
Title of chapter Model-based clustering
Title of book Comprehensive chemometrics: chemical and biochemical data analysis
Place of Publication Oxford, U.K.
Publisher Elsevier Science
Publication Year 2009
Sub-type Research book chapter (original research)
DOI 10.1016/B978-044452701-1.00068-5
ISBN 9780444527011
Editor Steven D. Brown
Roma Tauler
Beata Walczak
Volume number 2
Start page 655
End page 681
Total pages 27
Total chapters 25
Collection year 2010
Language eng
Subjects B1
970101 Expanding Knowledge in the Mathematical Sciences
010401 Applied Statistics
Abstract/Summary Finite mixture models are being commonly used in a wide range of applications in practice concerning density estimation and clustering. An attractive feature of this approach to clustering is that it provides a sound statistical framework in which to assess the important question of how many clusters are there in the data and their validity.
Keyword Finite mixture modelling
Maximum likelihood
EM algorithm
Normal components
Factor analyzers
Choice of number of components
Q-Index Code B1
Q-Index Status Confirmed Code

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Created: Wed, 07 Apr 2010, 15:28:43 EST by Kay Mackie on behalf of School of Mathematics & Physics