A feature group weighting method for subspace clustering of high-dimensional data

This paper proposes a new method to weight subspaces in feature groups and individual features for clustering high-dimensional data. In this method, the features of high-dimensional data are divided into feature groups, based on their natural characteristics. Two types of weights are introduced to t...

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Veröffentlicht in:	Pattern recognition Jg. 45; H. 1; S. 434 - 446
Hauptverfasser:	Chen, Xiaojun, Ye, Yunming, Xu, Xiaofei, Huang, Joshua Zhexue
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 2012 Elsevier
Schlagworte:	Algorithms Applied sciences Clustering Clusters Data mining Exact sciences and technology Feature weighting High-dimensional data analysis Information theory Information, signal and communications theory k-Means Noise Optimization Pattern recognition Signal and communications theory Signal representation. Spectral analysis Signal, noise Subspace clustering Subspaces Telecommunications and information theory Weighting methods Subspace clustering High-dimensional data analysis Data mining k-Means Feature weighting Performance evaluation Data analysis Automatic classification Subspace method Data processing K means algorithm Signal classification Optimization Weighting Dimensional analysis
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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