Combining K-Means and Optimization Algorithms for data clustering

Data clustering is related to the split of a set of objects into smaller groups with common features. Several optimization techniques have been proposed to increase the performance of clustering algorithms. Swarm Intelligence algorithms are concerned with optimization problems and they have been successfully applied to different domains. In this work, a Swarm Clustering Algorithm is proposed based on the standard K-Means clustering algorithms, which are used as fitness functions for a Swarm Intelligence algorithm. The motivation is to exploit the search capability of Swarm Intelligence algorithms and to avoid the major limitation of falling into locally optimal values of the K-Means algorithm. Because of the inherent parallel nature of the Swarm Intelligence algorithms, since the fitness function can be evaluated for each individual in an isolated manner, we have developed the parallel implementation, comparing the performances with their algorithm. Experiments with 2 bench-mark datasets have shown similar or slightly better quality of the results compared to standard K-Means algorithm and other algorithm. There results of using proposed algorithm for clustering are promising