A genetic approach to the automatic clustering problem

In solving the clustering problem, traditional methods, for example, the K- means algorithm and its variants, usually ask the user to provide the numbe r of clusters. Unfortunately, the number of clusters in general is unknown to the user. Therefore, clustering becomes a tedious trial-and-error work a nd the clustering result is often not very promising especially when the nu mber of clusters is large and not easy to guess. In this paper, we propose a genetic algorithm for the clustering problem. This algorithm is suitable for clustering the data with compact spherical clusters. It can be used in two ways. One is the user-controlled clustering, where the user may control the result of clustering by varying the values of the parameter, w. A smal l value of w results in a larger number of compact clusters, while a large value of w results in a smaller number of looser clusters. The other is an automatic clustering, where a heuristic strategy is applied to find a good clustering. Experimental results are given to illustrate the effectiveness of this genetic clustering algorithm. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.