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Subspace clustering with automatic feature grouping
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DOI：10.1016/j.patcog.2015.05.016.png)
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Abstract
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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. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Data clustering
Subspace clustering
k-means
Feature group
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