A comparison of the classification capabilities of the 1-dimensional Kohonen neural network with two partitioning and three hierarchical cluster analysis algorithms

Niels G. Waller, Heather A. Kaiser, Janine B. Illian, Mike Manry

Research output: Contribution to journalReview articlepeer-review

49 Scopus citations

Abstract

Neural Network models are commonly used for cluster analysis in engineering, computational neuroscience, and the biological sciences, although they are rarely used in the social sciences. In this study we compare the classification capabilities of the 1-dimensional Kohonen neural network with two partitioning (Hartigan and Späth k-means) and three hierarchical (Ward's, complete linkage, and average linkage) cluster methods in 2,580 data sets with known cluster structure. Overall, the performance of the Kohonen networks was similar to, or better than, the performance of the other methods.

Original languageEnglish (US)
Pages (from-to)5-22
Number of pages18
JournalPsychometrika
Volume63
Issue number1
DOIs
StatePublished - Mar 1998
Externally publishedYes

Keywords

  • Cluster analysis
  • Kohonen models
  • Neural networks

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