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Subspace clustering with automatic feature grouping.
- Source :
-
Pattern Recognition . Nov2015, Vol. 48 Issue 11, p3703-3713. 11p. - Publication Year :
- 2015
-
Abstract
- This paper proposes a subspace clustering algorithm with automatic feature grouping for clustering high-dimensional data. In this algorithm, a new component is introduced into the objective function to capture the feature groups and a new iterative process is defined to optimize the objective function so that the features of high-dimensional data are grouped automatically. Experiments on both synthetic data and real data show that the new algorithm outperforms the FG- k -means algorithm in terms of accuracy and choice of parameters. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 48
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 108433434
- Full Text :
- https://doi.org/10.1016/j.patcog.2015.05.016