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14 June 1996 Self-organizing k-means classification algorithm
Rustom Mamlook, Wiley E. Thompson
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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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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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KEYWORDS
Data centers
Statistical analysis
Computer engineering
Machine learning
Applied sciences
Data analysis
Lanthanum
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