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A Comparison of the Classification Capabilities of the 1-Dimensional Kohonen Neural Network with Two Pratitioning and Three Hierarchical Cluster Analysis Algorithms

Published online by Cambridge University Press:  01 January 2025

Niels G. Waller ,
Heather A. Kaiser ,
Janine B. Illian  and
Mike Manry
Niels G. Waller*
Affiliation:
University of California, Davis
Heather A. Kaiser
Affiliation:
University of California, Davis
Janine B. Illian
Affiliation:
University of Duesseldorf
Mike Manry
Affiliation:
University of Texas at Arlington
*
Requests for reprints should be sent to Niels G. Waller, Department of Psychology, University of California, Davis CA 95616. E-mail: ngwaller@ucdavis.edu.
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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.

Keywords

neural networksKohonen modelscluster analysis

Information

Type
Original Paper
Information
Psychometrika , Volume 63 , Issue 1 , March 1998 , pp. 5 - 22
Copyright
Copyright © 1998 The Psychometric Society

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