Data-clustering algorithm using a self-organizing method

Rustom Mamlook; Wiley E. Thompson

doi:10.1117/12.154977

3 September 1993 Data-clustering algorithm using a self-organizing method

Rustom Mamlook, Wiley E. Thompson

Proceedings Volume 1955, Signal Processing, Sensor Fusion, and Target Recognition II; (1993) https://doi.org/10.1117/12.154977
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

Rustom Mamlook and Wiley E. Thompson "Data-clustering algorithm using a self-organizing method", Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); https://doi.org/10.1117/12.154977

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