Accession Number : ADA113422

Title :   Multi-Sample Cluster Analysis with Varying Parameters Using Akaike's Information Criterion.

Descriptive Note : Technical rept.,

Corporate Author : ILLINOIS UNIV AT CHICAGO CIRCLE DEPT OF QUANTITATIVE METHODS

Personal Author(s) : Bozdogan,Hamparsum ; Sclove,Stanley L

PDF Url : ADA113422

Report Date : 08 Mar 1982

Pagination or Media Count : 29

Abstract : Multi-sample cluster analysis, the problem of grouping samples, is studied from an information-theoretic viewpoint via Akaike's Information Criterion (AIC). This criterion combines the maximum value of the likelihood with the number of parameters used in achieving that value. The multi-sample cluster problem is defined, and AIC is developed for this problem. The form of AIC is derived in the univariate model with varying means and variances, and in the multivariate model with varying mean vectors and variance-covariance matrices. Numerical examples are presented and results are shown to demonstrate the utility of AIC in identifying the best clustering alternatives. (Author)

Descriptors :   *Multivariate analysis, *Clustering, Sampling, Parameters, Maximum likelihood estimation, Variational principles, Mathematical models, Covariance, Information theory, Comparison, Identification

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE