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PATTERN CLUSTERING BY MULTIVARIATE MIXTURE ANALYSIS

Wolfe, John H.·1970·Multivariate Behavioral Research

Abstract

Cluster analysis is reformulated as a problem of estimating the para- meters of a mixture of multivariate distributions. The maximum-likelihood theory and numerical solution techniques are developed for a fairly general class of distributions. The theory is applied to mixtures of multivariate nor- mals (NORMIX) and mixtures of multivariate Bernoulli distributions (Latent Classes). The feasibility of the procedures is demonstrated by two examples of computer solutions for normal mixture models of the Fisher Iris data and of artifjcially generated clusters with unequal covariance matrices.

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Metadata

Authors
Wolfe, J. H.
Venue
Multivariate Behavioral Research
Published
1970-04
Volume
5
Issue
3
Pages
329-350
ISSN
  • 0027-3171 (ISSN-L)
  • 1532-7906
OpenAlex
W2118570622
Cited by
609
References
0
FWCI
7.16
Type
journal article
Language
en
Open access
closed

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