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Applied Computational Intelligence and Soft Computing
Volume 2013 (2013), Article ID 863146, 12 pages
http://dx.doi.org/10.1155/2013/863146
Research Article

Subspace Clustering of High-Dimensional Data: An Evolutionary Approach

1Department of Computer Science and Engineering, Faculty of Engineering and Technology, Mody Institute of Technology and Science, Lakshmangarh, Rajasthan 332311, India
2School of Computer Engineering, KIIT University, Bhubaneswar 751024, India

Received 21 August 2013; Revised 20 October 2013; Accepted 11 November 2013

Academic Editor: Sebastian Ventura

Copyright © 2013 Singh Vijendra and Sahoo Laxman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Clustering high-dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full-dimensional space. In this paper, we have presented a robust multi objective subspace clustering (MOSCL) algorithm for the challenging problem of high-dimensional clustering. The first phase of MOSCL performs subspace relevance analysis by detecting dense and sparse regions with their locations in data set. After detection of dense regions it eliminates outliers. MOSCL discovers subspaces in dense regions of data set and produces subspace clusters. In thorough experiments on synthetic and real-world data sets, we demonstrate that MOSCL for subspace clustering is superior to PROCLUS clustering algorithm. Additionally we investigate the effects of first phase for detecting dense regions on the results of subspace clustering. Our results indicate that removing outliers improves the accuracy of subspace clustering. The clustering results are validated by clustering error (CE) distance on various data sets. MOSCL can discover the clusters in all subspaces with high quality, and the efficiency of MOSCL outperforms PROCLUS.