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Your search keyword '"support vector clustering"' showing total 2 results
Start Over Descriptor "support vector clustering" Journal modern physics letters b
2 results on '"support vector clustering"'

1. Community detection in complex networks using proximate support vector clustering.

Author

Feifan Wang, Baihai Zhang, Senchun Chai, and Yuanqing Xia

Subjects
*HIERARCHICAL clustering (Cluster analysis), *CLUSTER analysis (Statistics), *SUPPORT vector machines, *COMPUTER networks, *ALGORITHMS
Abstract

Community structure, one of the most attention attracting properties in complex net-works, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts. [ABSTRACT FROM AUTHOR]

Published
2018
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2. Community detection in complex networks using proximate support vector clustering

Author

Yuanqing Xia, Baihai Zhang, Feifan Wang, and Senchun Chai

Subjects
0301 basic medicine, Computer science, Community structure, Cornerstone, Support vector clustering, Statistical and Nonlinear Physics, Complex network, Condensed Matter Physics, 01 natural sciences, Data science, 03 medical and health sciences, 030104 developmental biology, 0103 physical sciences, 010306 general physics
Abstract

Community structure, one of the most attention attracting properties in complex networks, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts.

Published
2018
Full Text
View/download PDF

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