Self-organizing k-means classification algorithm

Rustom Mamlook; Wiley E. Thompson

doi:10.1117/12.243197

14 June 1996 Self-organizing k-means classification algorithm

Rustom Mamlook, Wiley E. Thompson

Proceedings Volume 2755, Signal Processing, Sensor Fusion, and Target Recognition V; (1996) https://doi.org/10.1117/12.243197
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

A self-organizing k-means algorithm to classify the inputs (data) into classes is presented. This algorithm provides solutions to the problems that the k-means classification algorithm faces. The k-means classification algorithm has the problem of selecting the threshold(s). It also requires that the number of classes be known a priori. This algorithm forms clusters, removes noise, and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of input data. The algorithm consists of two phases. The first phase is similar to the Carpenter/Grossberg classifier, and the second phase is a modified version of the k-means algorithm. An example is given to illustrate the application of this algorithm and to compare this algorithm with the k-means algorithm.

Citation Download Citation

Rustom Mamlook and Wiley E. Thompson "Self-organizing k-means classification algorithm", Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); https://doi.org/10.1117/12.243197

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