Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/629297
Title: Constrained Clustering With Nonnegative Matrix Factorization
Authors: Xianchao Zhang;Linlin Zong;Xinyue Liu;Jiebo Luo
subject: symmetric NMF (SymNMF).|semi-supervised learning|Constrained clustering|nonnegative matrix factorization (NMF)
Year: 2016
Publisher: IEEE
Abstract: Nonnegative matrix factorization (NMF) and symmetric NMF (SymNMF) have been shown to be effective for clustering linearly separable data and nonlinearly separable data, respectively. Nevertheless, many practical applications demand constrained algorithms in which a small number of constraints in the form of must-link and cannot-link are available. In this paper, we propose an NMF-based constrained clustering framework in which the similarity between two points on a must-link is enforced to approximate 1 and the similarity between two points on a cannot-link is enforced to approximate 0. We then formulate the framework using NMF and SymNMF to deal with clustering of linearly separable data and nonlinearly separable data, respectively. Furthermore, we present multiplicative update rules to solve them and show the correctness and convergence. Experimental results on various text data sets, University of California, Irvine (UCI) data sets, and gene expression data sets demonstrate the superiority of our algorithms over existing constrained clustering algorithms.
URI: http://localhost/handle/Hannan/163959
http://localhost/handle/Hannan/629297
ISSN: 2162-237X
2162-2388
volume: 27
issue: 7
Appears in Collections:2016

Files in This Item:
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7167693.pdf5.77 MBAdobe PDFThumbnail
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Title: Constrained Clustering With Nonnegative Matrix Factorization
Authors: Xianchao Zhang;Linlin Zong;Xinyue Liu;Jiebo Luo
subject: symmetric NMF (SymNMF).|semi-supervised learning|Constrained clustering|nonnegative matrix factorization (NMF)
Year: 2016
Publisher: IEEE
Abstract: Nonnegative matrix factorization (NMF) and symmetric NMF (SymNMF) have been shown to be effective for clustering linearly separable data and nonlinearly separable data, respectively. Nevertheless, many practical applications demand constrained algorithms in which a small number of constraints in the form of must-link and cannot-link are available. In this paper, we propose an NMF-based constrained clustering framework in which the similarity between two points on a must-link is enforced to approximate 1 and the similarity between two points on a cannot-link is enforced to approximate 0. We then formulate the framework using NMF and SymNMF to deal with clustering of linearly separable data and nonlinearly separable data, respectively. Furthermore, we present multiplicative update rules to solve them and show the correctness and convergence. Experimental results on various text data sets, University of California, Irvine (UCI) data sets, and gene expression data sets demonstrate the superiority of our algorithms over existing constrained clustering algorithms.
URI: http://localhost/handle/Hannan/163959
http://localhost/handle/Hannan/629297
ISSN: 2162-237X
2162-2388
volume: 27
issue: 7
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7167693.pdf5.77 MBAdobe PDFThumbnail
Preview File
Title: Constrained Clustering With Nonnegative Matrix Factorization
Authors: Xianchao Zhang;Linlin Zong;Xinyue Liu;Jiebo Luo
subject: symmetric NMF (SymNMF).|semi-supervised learning|Constrained clustering|nonnegative matrix factorization (NMF)
Year: 2016
Publisher: IEEE
Abstract: Nonnegative matrix factorization (NMF) and symmetric NMF (SymNMF) have been shown to be effective for clustering linearly separable data and nonlinearly separable data, respectively. Nevertheless, many practical applications demand constrained algorithms in which a small number of constraints in the form of must-link and cannot-link are available. In this paper, we propose an NMF-based constrained clustering framework in which the similarity between two points on a must-link is enforced to approximate 1 and the similarity between two points on a cannot-link is enforced to approximate 0. We then formulate the framework using NMF and SymNMF to deal with clustering of linearly separable data and nonlinearly separable data, respectively. Furthermore, we present multiplicative update rules to solve them and show the correctness and convergence. Experimental results on various text data sets, University of California, Irvine (UCI) data sets, and gene expression data sets demonstrate the superiority of our algorithms over existing constrained clustering algorithms.
URI: http://localhost/handle/Hannan/163959
http://localhost/handle/Hannan/629297
ISSN: 2162-237X
2162-2388
volume: 27
issue: 7
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7167693.pdf5.77 MBAdobe PDFThumbnail
Preview File