(untitled) (International Journal of Engineering and Computer Science, 2017)
The paper deals with the applicability of GA to clustering and compares it with the standard K-means clustering technique. K-means clustering results are extremely sensitive to the initial centroids, so many a times it results in sub-optimal solutions. On the other hand the GA approach results in optimal solutions and finds globally optimal disjoint partitions. Fitness calculated on the basis of intra-cluster and inter-cluster distance is the performance evaluation standard in this paper. The experimental results show that the proposed GA is more effective than K-means and converges to more accurate clusters.