An Enhanced Clustering of High Dimensional Datasets Using Unsupervised Quick Reduct Algorithm (USQR) With Rough Set Theory

Abstract

The performance of K-means clustering algorithm is poor for high dimensions data set. The goal of this paper is to reduce the high dimensional data to a meaningful low dimensional data representation, so that the efficiency of clustering algorithm will be elevated. Hence to improve the efficiency of clustering analysis, unsupervised quick reduct algorithm (USQR) is used for selecting the features from high dimensional data. Then the selected features are used to find the initial centroid using k-MAM initialization technique for k-means. The initial centroids are finally used to find the clusters. The results are compared to k-means and k-MAM with USQR so that outperforms well, in terms of accuracy and number of iterations compared to the k-means, for high dimensional data.