Subspace Clustering of High-Dimensional Data: An Evolutionary Approach
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 presen...
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Wiley
2013-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2013/863146 |
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| author | Singh Vijendra Sahoo Laxman |
| author_facet | Singh Vijendra Sahoo Laxman |
| author_sort | Singh Vijendra |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f7c2aae2e309402884caf4898141227e |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-f7c2aae2e309402884caf4898141227e2025-02-03T01:23:40ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322013-01-01201310.1155/2013/863146863146Subspace Clustering of High-Dimensional Data: An Evolutionary ApproachSingh Vijendra0Sahoo Laxman1Department of Computer Science and Engineering, Faculty of Engineering and Technology, Mody Institute of Technology and Science, Lakshmangarh, Rajasthan 332311, IndiaSchool of Computer Engineering, KIIT University, Bhubaneswar 751024, IndiaClustering 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.http://dx.doi.org/10.1155/2013/863146 |
| spellingShingle | Singh Vijendra Sahoo Laxman Subspace Clustering of High-Dimensional Data: An Evolutionary Approach Applied Computational Intelligence and Soft Computing |
| title | Subspace Clustering of High-Dimensional Data: An Evolutionary Approach |
| title_full | Subspace Clustering of High-Dimensional Data: An Evolutionary Approach |
| title_fullStr | Subspace Clustering of High-Dimensional Data: An Evolutionary Approach |
| title_full_unstemmed | Subspace Clustering of High-Dimensional Data: An Evolutionary Approach |
| title_short | Subspace Clustering of High-Dimensional Data: An Evolutionary Approach |
| title_sort | subspace clustering of high dimensional data an evolutionary approach |
| url | http://dx.doi.org/10.1155/2013/863146 |
| work_keys_str_mv | AT singhvijendra subspaceclusteringofhighdimensionaldataanevolutionaryapproach AT sahoolaxman subspaceclusteringofhighdimensionaldataanevolutionaryapproach |