Author(s)

P. Fränti & O. Virmajoki

Abstract

Genetic algorithm using iterative shrinking for solving clustering problems P. Frwti & 0. Virrnajoki Department of Computer Science, University of Joensuu, Finland Abstract An iterative shrinking algorithm has been recently proposed to replace mergebased agglomerative clustering. In this work, we first extend this idea to the case where the number of clusters must also be solved. We then integrate the method within genetic algorithms that uses iterative shrinking for crossover. The proposed method outperforms all other clustering algorithms we have tested. We therefore conclude that this method is the best existing clustering algorithm in terms of minimizing the distortion function value. 1 Introduction Clustering is an important problem that must often be solved as a part of more complicated tasks in pattern recognition, image analysis, and other fields of science and engineering [I, 2, 31. Clustering is also needed for designing a codebook in vector quantization [4]. Clustering problem is de

Keywords