Multi-sphere support vector clustering

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

Research output: A Conference proceeding or a Chapter in BookConference contribution

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 languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication18th International Conference, ICONIP 2011, Proceedings, Part II
EditorsBao-Liang Lu, Liqing Zhang, James Kwok
Place of PublicationBerlin, Germany
Pages537-544
Number of pages8
ISBN (Electronic)9783642249587
DOIs
Publication statusPublished - 2011
Event18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China
Duration: 13 Nov 201117 Nov 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7063 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Neural Information Processing, ICONIP 2011
CountryChina
CityShanghai
Period13/11/1117/11/11

Fingerprint

Support Vector
Clustering
Space Form
Feature Space
Clustering Methods
Cluster analysis
Data Distribution
Cluster Analysis
High-dimensional
Demonstrate
Experiment
Experiments

Cite this

Le, T., Tran, D., Nguyen, P., Ma, W., & Sharma, D. (2011). Multi-sphere support vector clustering. In B-L. Lu, L. Zhang, & J. Kwok (Eds.), Neural Information Processing: 18th International Conference, ICONIP 2011, Proceedings, Part II (pp. 537-544). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7063 LNCS, No. PART 2). Berlin, Germany. https://doi.org/10.1007/978-3-642-24958-7_62
Le, Trung ; Tran, Dat ; Nguyen, Phuoc ; Ma, Wanli ; Sharma, Dharmendra. / Multi-sphere support vector clustering. Neural Information Processing: 18th International Conference, ICONIP 2011, Proceedings, Part II. editor / Bao-Liang Lu ; Liqing Zhang ; James Kwok. Berlin, Germany, 2011. pp. 537-544 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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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 B-L Lu, L Zhang & J Kwok (eds), Neural Information Processing: 18th International Conference, ICONIP 2011, Proceedings, Part II. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7063 LNCS, Berlin, Germany, pp. 537-544, 18th International Conference on Neural Information Processing, ICONIP 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; Ma, Wanli; Sharma, Dharmendra.

Neural Information Processing: 18th International Conference, ICONIP 2011, Proceedings, Part II. ed. / Bao-Liang Lu; Liqing Zhang; James Kwok. Berlin, Germany, 2011. p. 537-544 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7063 LNCS, No. PART 2).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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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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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.

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Le T, Tran D, Nguyen P, Ma W, Sharma D. Multi-sphere support vector clustering. In Lu B-L, Zhang L, Kwok J, editors, Neural Information Processing: 18th International Conference, ICONIP 2011, Proceedings, Part II. Berlin, Germany. 2011. p. 537-544. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-24958-7_62