A K-means based genetic algorithm for data clustering

Abstract

A genetic algorithm, that exploits the K-means principles for dividing objects in groups having high similarity, is proposed. The method evolves a population of chromosomes, each representing a division of objects in a different number of clusters. A group-based crossover, enriched with the one-step K-means operator, and a mutation strategy that reassigns objects to clusters on the base of their distance to the clusters computed so far, allow the approach to determine the best number of groups present in the dataset. The method has been experimented with four different fitness functions on both synthetic and real-world datasets, for which the ground-truth division is known, and compared with the K-means method. Results show that the approach obtains higher values of evaluation indexes than that obtained by the K-means method.