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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Vydané v:	Pattern recognition Ročník 45; číslo 1; s. 434 - 446
Hlavní autori:	Chen, Xiaojun, Ye, Yunming, Xu, Xiaofei, Huang, Joshua Zhexue
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 2012 Elsevier
Predmet:	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
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Abstract	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 the clustering process to simultaneously identify the importance of feature groups and individual features in each cluster. A new optimization model is given to define the optimization process and a new clustering algorithm FG- k-means is proposed to optimize the optimization model. The new algorithm is an extension to k-means by adding two additional steps to automatically calculate the two types of subspace weights. A new data generation method is presented to generate high-dimensional data with clusters in subspaces of both feature groups and individual features. Experimental results on synthetic and real-life data have shown that the FG- k-means algorithm significantly outperformed four k-means type algorithms, i.e., k-means, W- k-means, LAC and EWKM in almost all experiments. The new algorithm is robust to noise and missing values which commonly exist in high-dimensional data. ► Its first method to weight subspaces of feature groups and individual features. ► We propose the FG- k-means algorithm to optimize the new model. ► We present a method to generate data with clusters in subspaces of feature groups. ► We present experimental results on synthetic and real-life data of FG- k-means. ► Experimental results demonstrate that it can be used for feature selection.
AbstractList	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 the clustering process to simultaneously identify the importance of feature groups and individual features in each cluster. A new optimization model is given to define the optimization process and a new clustering algorithm FG-k-means is proposed to optimize the optimization model. The new algorithm is an extension to k-means by adding two additional steps to automatically calculate the two types of subspace weights. A new data generation method is presented to generate high-dimensional data with clusters in subspaces of both feature groups and individual features. Experimental results on synthetic and real-life data have shown that the FG-k-means algorithm significantly outperformed four k-means type algorithms, i.e., k-means, W-k-means, LAC and EWKM in almost all experiments. The new algorithm is robust to noise and missing values which commonly exist in 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 the clustering process to simultaneously identify the importance of feature groups and individual features in each cluster. A new optimization model is given to define the optimization process and a new clustering algorithm FG- k-means is proposed to optimize the optimization model. The new algorithm is an extension to k-means by adding two additional steps to automatically calculate the two types of subspace weights. A new data generation method is presented to generate high-dimensional data with clusters in subspaces of both feature groups and individual features. Experimental results on synthetic and real-life data have shown that the FG- k-means algorithm significantly outperformed four k-means type algorithms, i.e., k-means, W- k-means, LAC and EWKM in almost all experiments. The new algorithm is robust to noise and missing values which commonly exist in high-dimensional data. ► Its first method to weight subspaces of feature groups and individual features. ► We propose the FG- k-means algorithm to optimize the new model. ► We present a method to generate data with clusters in subspaces of feature groups. ► We present experimental results on synthetic and real-life data of FG- k-means. ► Experimental results demonstrate that it can be used for feature selection.
Author	Ye, Yunming Xu, Xiaofei Chen, Xiaojun Huang, Joshua Zhexue
Author_xml	– sequence: 1 givenname: Xiaojun surname: Chen fullname: Chen, Xiaojun email: xjchen.hitsz@gmail.com organization: Shenzhen Graduate School, Harbin Institute of Technology, China – sequence: 2 givenname: Yunming surname: Ye fullname: Ye, Yunming email: yeyunming@hit.edu.cn organization: Shenzhen Graduate School, Harbin Institute of Technology, China – sequence: 3 givenname: Xiaofei surname: Xu fullname: Xu, Xiaofei email: xiaofei@hit.edu.cn organization: Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China – sequence: 4 givenname: Joshua Zhexue surname: Huang fullname: Huang, Joshua Zhexue email: zx.huang@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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Keywords	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
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Snippet	This paper proposes a new method to weight subspaces in feature groups and individual features for clustering high-dimensional data. In this method, the...
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SubjectTerms	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
Title	A feature group weighting method for subspace clustering of high-dimensional data
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