Clustering using a coarse-grained parallel genetic algorithm: a preliminary study

Genetic Algorithms (GA) are useful in solving complex optimization problems. By posing pattern clustering as an optimization problem, GAs can be used to obtain an optimal minimum squared-error partitions. In order to improve the total execution time, a distributed algorithm has been developed using the divide and conquer approach. Using a standard communication library called PVM, the distributed algorithm has been implemented on a workstation cluster. The GA approach gives better quality clusters for many data sets compared to a standard K-Means clustering algorithm. We have achieved a near linear speedup for the distributed implementation.