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Subspace Clustering of High-Dimensional Data : An Evolutionary Approach
Joint Authors
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-31
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Vijendra, Singh& Laxman, Sahoo. 2013. Subspace Clustering of High-Dimensional Data : An Evolutionary Approach. Applied Computational Intelligence and Soft Computing،Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-504355
Modern Language Association (MLA)
Vijendra, Singh& Laxman, Sahoo. Subspace Clustering of High-Dimensional Data : An Evolutionary Approach. Applied Computational Intelligence and Soft Computing No. 2013 (2013), pp.1-12.
https://search.emarefa.net/detail/BIM-504355
American Medical Association (AMA)
Vijendra, Singh& Laxman, Sahoo. Subspace Clustering of High-Dimensional Data : An Evolutionary Approach. Applied Computational Intelligence and Soft Computing. 2013. Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-504355
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-504355