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A Study of the Classification Capabilities of Neural Networks Using Unsupervised Learning: A Comparison with K-means Clustering

Published online by Cambridge University Press:  01 January 2025

P. V. (Sundar) Balakrishnan ,
Martha C. Cooper ,
Varghese S. Jacob  and
Phillip A. Lewis
P. V. (Sundar) Balakrishnan
Affiliation:
Business Administration Program, University of Washington, Bothell
Martha C. Cooper*
Affiliation:
Department of Marketing, The Ohio State University
Varghese S. Jacob
Affiliation:
Department of Accounting and Mis, The Ohio State University
Phillip A. Lewis
Affiliation:
Department of Marketing, Rowan College of New Jersey
*
Requests for reprints should be sent to Martha Cooper, College of Business, The Ohio State University, 1775 College Road, Columbus, OH 43210-1399.
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Abstract

Several neural networks have been proposed in the general literature for pattern recognition and clustering, but little empirical comparison with traditional methods has been done. The results reported here compare neural networks using Kohonen learning with a traditional clustering method (K-means) in an experimental design using simulated data with known cluster solutions. Two types of neural networks were examined, both of which used unsupervised learning to perform the clustering. One used Kohonen learning with a conscience and the other used Kohonen learning without a conscience mechanism. The performance of these nets was examined with respect to changes in the number of attributes, the number of clusters, and the amount of error in the data. Generally, the K-means procedure had fewer points misclassified while the classification accuracy of neural networks worsened as the number of clusters in the data increased from two to five.

Keywords

K-meansKohonenMonte Carlo simulationneural networksnonhierarchical clustering

Information

Type
Original Paper
Information
Psychometrika , Volume 59 , Issue 4 , December 1994 , pp. 509 - 525
Copyright
Copyright © 1994 The Psychometric Society

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Footnotes

Acknowledgements: Sara Dickson, Vidya Nair, and Beth Means assisted with the neural network analyses.

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