Clustering of datasets using PSO-K-Means and PCA-K-means

Abstract Cluster analysis plays indispensable role in obtaining knowledge from data, being the first step in data mining and knowledge discovery. The purpose of data clustering is to reveal the data patterns and gain some initial insights regarding data distribution. K-means is one of the widely used partitional clustering algorithms and it is more sensitive to outliers and do not work well with high dimensional data. In this paper, K-means has been integrated with other approaches to overcome the shortcomings hereby improving the accuracy of clustering. In this paper, basic k-means and the combination of k-means with PCA and PSO are applied on various datasets from UCI repository. The experimental results of this paper show that PSO-K-means and PCA-KMeans improves the performance of basic K-means in terms of accuracy and computational time