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Proceedings Paper

Data-clustering algorithm using a self-organizing method
Author(s): Rustom Mamlook; Wiley E. Thompson
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Paper Abstract

A new data clustering algorithm using a self organizing method is presented. This algorithm forms clusters and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of data. This algorithm differs from the K-means algorithm and other clustering algorithms in that the number of desired clusters is not required to be known a priori. It also removes noise and is fast. The convergence of the algorithm is shown. An example is given to show the application of the algorithm to clustering data and to compare the results obtained using this algorithm with those obtained using the K-means algorithm.

Paper Details

Date Published: 3 September 1993
PDF: 6 pages
Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); doi: 10.1117/12.154977
Show Author Affiliations
Rustom Mamlook, New Mexico State Univ. (Jordan)
Wiley E. Thompson, New Mexico State Univ. (United States)


Published in SPIE Proceedings Vol. 1955:
Signal Processing, Sensor Fusion, and Target Recognition II
Ivan Kadar; Vibeke Libby, Editor(s)

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