Multi-Sample Cluster Analysis 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 both the multivariate analysis of variance (MANOVA) model and in the multivariate model with varying mean vectors and variance-covariance matrices. Numerical examples are presented for AIC and another criterion called w-square. The results demonstrate the utility of AIC in identifying the best clustering alternatives. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 20, 1982
Accession Number
ADA124432

Entities

People

  • Hamparsum Bozdogan
  • Stanley L. Scolve

Organizations

  • University of Illinois at Chicago

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Clustering
  • Covariance
  • Data Science
  • Data Sets
  • Identification
  • Illinois
  • Information Science
  • Massachusetts
  • Mathematics
  • Military Research
  • Multivariate Analysis
  • Statistical Analysis
  • Statistics
  • United States
  • Universities

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.