Rough Cluster Algorithm Based on Kernel Function
By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis. Through using Mercer kernel functions, samples in the original space were mapped into a high-dimensional feature spac...
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Published in | Rough Sets and Knowledge Technology pp. 172 - 179 |
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Main Authors | , , , , |
Format | Book Chapter |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Abstract | By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis. Through using Mercer kernel functions, samples in the original space were mapped into a high-dimensional feature space, which the difference among these samples in sample space was strengthened through kernel mapping, combining rough set with k-means to cluster in feature space. These samples were assigned into up-approximation or low-approximation of corresponding clustering centers, and then these data that were in up-approximation and low-approximation were combined and to update cluster center. Through this method, clustering precision was improved, clustering convergence speed was fast compared with classical clustering algorithms The results of simulation experiments show the feasibility and effectiveness of the kernel clustering algorithm. |
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AbstractList | By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis. Through using Mercer kernel functions, samples in the original space were mapped into a high-dimensional feature space, which the difference among these samples in sample space was strengthened through kernel mapping, combining rough set with k-means to cluster in feature space. These samples were assigned into up-approximation or low-approximation of corresponding clustering centers, and then these data that were in up-approximation and low-approximation were combined and to update cluster center. Through this method, clustering precision was improved, clustering convergence speed was fast compared with classical clustering algorithms The results of simulation experiments show the feasibility and effectiveness of the kernel clustering algorithm. |
Author | Deng, Fang’an Zhou, Tao Zhang, Yanning Lu, Huiling Wang, Fengxiao |
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Copyright | Springer-Verlag Berlin Heidelberg 2008 |
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DOI | 10.1007/978-3-540-79721-0_27 |
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Discipline | Engineering Computer Science |
EISBN | 9783540797210 3540797211 |
EISSN | 1611-3349 |
Editor | Wang, Guoyin Li, Tianrui Miao, Duoqian Yao, Yiyu Grzymala-Busse, Jerzy W. Skowron, Andrzej |
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EndPage | 179 |
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PublicationSeriesSubtitle | Lecture Notes in Artificial Intelligence |
PublicationSeriesTitle | Lecture Notes in Computer Science |
PublicationSubtitle | Third International Conference, RSKT 2008, Chengdu, China, May 17-19, 2008. Proceedings |
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Snippet | By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed... |
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StartPage | 172 |
SubjectTerms | K-means Kernel clustering algorithm Kernel methods Rough clustering Rough set |
Title | Rough Cluster Algorithm Based on Kernel Function |
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