Clustering Using a Combination of Particle Swarm Optimization and K-means

Abstract

AbstractClustering is an unsupervised kind of grouping of data points based on the similarity that exists between them. This paper applied a combination of particle swarm optimization and K-means for data clustering. The proposed approach tries to improve the performance of traditional partition clustering techniques such as K-means by avoiding the initial requirement of number of clusters or centroids for clustering. The proposed approach is evaluated using various primary and real-world datasets. Moreover, this paper also presents a comparison of results produced by the proposed approach and by the K-means based on clustering validity measures such as inter- and intra-cluster distances, quantization error, silhouette index, and Dunn index. The comparison of results shows that as the size of the dataset increases, the proposed approach produces significant improvement over the K-means partition clustering technique.