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 univariate and multivariate analysis of variance models. Numerical examples are presented and results are shown to demonstrate the utility of AIC in identifying the best clustering alternatives.

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Document Details

Document Type
Technical Report
Publication Date
Jan 30, 1982
Accession Number
ADA112469

Entities

People

  • Hamparsum Bozdogan
  • Stanley L. Sclove

Organizations

  • University of Illinois at Chicago

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Albumins
  • Analysis Of Variance
  • Blood Proteins
  • Clustering
  • Computations
  • Data Science
  • Data Sets
  • Illinois
  • Information Science
  • Massachusetts
  • Mathematics
  • Multivariate Analysis
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • United States
  • Universities

Readers

  • Regression Analysis.