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Cluster Analysis Based on Dimensional Information with Applications to Feature Selection and Classification

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3 Author(s)
Daryl J. Eigen ; University of Wisconsin-Milwaukee.; Bell Telephone Laboratories, Inc., Piscataway, N.J., 08857. ; Frederick R. Fromm ; Richard A. Northouse

A new clustering algorithm is presented that is based on dimensional information. The algorithm includes an inherent feature selection criterion, which is discussed. Further, a heuristic method for choosing the proper number of intervals for a frequency distribution histogram, a feature necessary for the algorithm, is presented. The algorithm, although usable as a stand-alone clustering technique, is then utilized as a global approximator. Local clustering techniques and configuration of a global-local scheme are discussed, and finally the complete global-local and feature selector configuration is shown in application to a real-time adaptive classification scheme for the analysis of remote sensed multispectral scanner data.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:SMC-4 ,  Issue: 3 )