GENETIC ALGORITHMS FOR LARGE-SCALE CLUSTERING PROBLEMS

We consider the clustering problem in the case where the distances bet ween elements are metric and both the number of attributes and the num ber of clusters are large. In this environment the genetic algorithm a pproach gives high quality clusterings, but at the expense of long run ning time. Three new and efficient crossover techniques are introduced here. The hybridization of the genetic algorithm and k-means algorith m is discussed.