The Classification and Mixture Maximum Likelihood Approaches to Cluster Analysis.

Abstract

A review is undertaken of two maximum likelihood approaches to cluster analysis, the so-called classification and mixture maximum likelihood methods. The basic assumptions of the two approaches and their associated properties are contrasted, in particular for multivariate normal component distributions. The problem of deciding how many clusters there are is discussed for each approach. Also, an account is given of the relative efficiency of the mixture approach to clustering. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 12, 1981
Accession Number
ADA102084

Entities

People

  • G. J. Mclachlan

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Clustering
  • Covariance
  • Data Science
  • Discrimination
  • Efficiency
  • Equations
  • Estimators
  • Information Science
  • Normal Distribution
  • Normality
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Samples
  • Statistics

Readers

  • Quantum Chemistry
  • Statistical inference.