An evolutionary technique based on K-means algorithm for optimal clustering in RN

Authors:
Sanghamitra Bandyopadhyay;Ujjwal Maulik
Affiliations:
Machine Intelligence Unit, Indian Statistical Institute, 203, B.T. Road, Calcutta 700108, India;Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani, Nadia, India
Venue:
Information Sciences—Applications: An International Journal
Year:
2002

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Abstract

A genetic algorithm-based efficient clustering technique that utilizes the principles of K-Means algorithm is described in this paper. The algorithm called KGA-clustering, while exploiting the searching capability of K-Means, avoids its major limitation of getting stuck at locally optimal values. Its superiority over the K-Means algorithm and another genetic algorithm-based clustering method, is extensively demonstrated for several artificial and real life data sets. A real life application of the KGA-clustering in classifying the pixels of a satellite image of a part of the city of Mumbai is provided.