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

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)

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Document Details

Document Type
Technical Report
Publication Date
Mar 08, 1982
Accession Number
ADA113422

Entities

People

  • Hamparsum Bozdogan
  • Stanley L. Sclove

Organizations

  • University of Illinois at Chicago

Tags

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Clustering
  • Computations
  • Covariance
  • Data Analysis
  • Data Science
  • Data Sets
  • Illinois
  • Information Science
  • Massachusetts
  • Mathematical Analysis
  • Mathematics
  • Multivariate Analysis
  • Observation
  • Statistical Analysis
  • United States
  • Universities

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.