Multi-sphere support vector clustering

Trung Le; Dat Tran; Phuoc Nguyen; Wanli Ma; Dharmendra Sharma

doi:10.1007/978-3-642-24958-7_62

Multi-sphere support vector clustering

Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma, Dharmendra Sharma

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

1 Citation (Scopus)

Abstract

Current support vector clustering method determines the smallest sphere that encloses the image of a dataset in feature space. This sphere when mapped back to data space will form a set of contours that can be interpreted as cluster boundaries for the dataset. However this method does not guarantee that the single sphere and the resulting cluster boundaries can best describe the dataset if there are some distinctive data distributions in this dataset. We propose multi-sphere support vector clustering to address this issue. Data points in data space are mapped to a high dimensional feature space and a set of smallest spheres that encloses the image of the dataset is determined. This set of spheres when mapped back to data space will form a set of contours that can be interpreted as cluster boundaries. Experiments on different datasets are performed to demonstrate that the proposed approach provides a better cluster analysis than the current support vector clustering method.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II
Publisher	Springer-Verlag London Ltd.
Pages	537-544
Number of pages	8
ISBN (Print)	9783642249570
DOIs	https://doi.org/10.1007/978-3-642-24958-7_62
Publication status	Published - 2011
Externally published	Yes
Event	International Conference on Neural Information Processing 2011 - Shanghai, China Duration: 13 Nov 2011 → 17 Nov 2011 Conference number: 18th https://link.springer.com/book/10.1007/978-3-642-24958-7 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	7063
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Neural Information Processing 2011
Abbreviated title	ICONIP 2011
Country/Territory	China
City	Shanghai
Period	13/11/11 → 17/11/11
Internet address	https://link.springer.com/book/10.1007/978-3-642-24958-7 (Proceedings)

Keywords

Cluster analysis
kernel method
support vector data description
support vector machine

Access to Document

10.1007/978-3-642-24958-7_62

Cite this

Le, T., Tran, D., Nguyen, P., Ma, W., & Sharma, D. (2011). Multi-sphere support vector clustering. In Neural Information Processing : 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II (pp. 537-544). (Lecture Notes in Computer Science ; Vol. 7063 ). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-642-24958-7_62

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title = "Multi-sphere support vector clustering",

abstract = "Current support vector clustering method determines the smallest sphere that encloses the image of a dataset in feature space. This sphere when mapped back to data space will form a set of contours that can be interpreted as cluster boundaries for the dataset. However this method does not guarantee that the single sphere and the resulting cluster boundaries can best describe the dataset if there are some distinctive data distributions in this dataset. We propose multi-sphere support vector clustering to address this issue. Data points in data space are mapped to a high dimensional feature space and a set of smallest spheres that encloses the image of the dataset is determined. This set of spheres when mapped back to data space will form a set of contours that can be interpreted as cluster boundaries. Experiments on different datasets are performed to demonstrate that the proposed approach provides a better cluster analysis than the current support vector clustering method.",

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Le, T, Tran, D, Nguyen, P, Ma, W & Sharma, D 2011, Multi-sphere support vector clustering. in Neural Information Processing : 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II. Lecture Notes in Computer Science , vol. 7063 , Springer-Verlag London Ltd., pp. 537-544, International Conference on Neural Information Processing 2011, Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24958-7_62

Multi-sphere support vector clustering. / Le, Trung; Tran, Dat; Nguyen, Phuoc et al.
Neural Information Processing : 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II. Springer-Verlag London Ltd., 2011. p. 537-544 (Lecture Notes in Computer Science ; Vol. 7063 ).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

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T1 - Multi-sphere support vector clustering

AU - Le, Trung

AU - Tran, Dat

AU - Nguyen, Phuoc

AU - Ma, Wanli

AU - Sharma, Dharmendra

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N2 - Current support vector clustering method determines the smallest sphere that encloses the image of a dataset in feature space. This sphere when mapped back to data space will form a set of contours that can be interpreted as cluster boundaries for the dataset. However this method does not guarantee that the single sphere and the resulting cluster boundaries can best describe the dataset if there are some distinctive data distributions in this dataset. We propose multi-sphere support vector clustering to address this issue. Data points in data space are mapped to a high dimensional feature space and a set of smallest spheres that encloses the image of the dataset is determined. This set of spheres when mapped back to data space will form a set of contours that can be interpreted as cluster boundaries. Experiments on different datasets are performed to demonstrate that the proposed approach provides a better cluster analysis than the current support vector clustering method.

AB - Current support vector clustering method determines the smallest sphere that encloses the image of a dataset in feature space. This sphere when mapped back to data space will form a set of contours that can be interpreted as cluster boundaries for the dataset. However this method does not guarantee that the single sphere and the resulting cluster boundaries can best describe the dataset if there are some distinctive data distributions in this dataset. We propose multi-sphere support vector clustering to address this issue. Data points in data space are mapped to a high dimensional feature space and a set of smallest spheres that encloses the image of the dataset is determined. This set of spheres when mapped back to data space will form a set of contours that can be interpreted as cluster boundaries. Experiments on different datasets are performed to demonstrate that the proposed approach provides a better cluster analysis than the current support vector clustering method.

KW - Cluster analysis

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Multi-sphere support vector clustering

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