A cooperative and penalized competitive learning approach to gaussian mixture clustering

Yiu Ming CHEUNG; Hong Jia

doi:10.1007/978-3-642-15825-4_58

A cooperative and penalized competitive learning approach to gaussian mixture clustering

Yiu Ming CHEUNG^*, Hong Jia

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Competitive learning approaches with penalization or cooperation mechanism have been applied to unsupervised data clustering due to their attractive ability of automatic cluster number selection. In this paper, we further investigate the properties of different competitive strategies and propose a novel learning algorithm called Cooperative and Penalized Competitive Learning (CPCL), which implements the cooperation and penalization mechanisms simultaneously in a single competitive learning process. The integration of these two different kinds of competition mechanisms enables the CPCL to have good convergence speed, precision and robustness. Experiments on Gaussian mixture clustering are performed to investigate the proposed algorithm. The promising results demonstrate its superiority.

Original language	English
Title of host publication	Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings
Pages	435-440
Number of pages	6
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-15825-4_58
Publication status	Published - 2010
Event	20th International Conference on Artificial Neural Networks, ICANN 2010 - Thessaloniki, Greece Duration: 15 Sept 2010 → 18 Sept 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	6354 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Artificial Neural Networks, ICANN 2010
Country/Territory	Greece
City	Thessaloniki
Period	15/09/10 → 18/09/10

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-642-15825-4_58

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CHEUNG, Y. M., & Jia, H. (2010). A cooperative and penalized competitive learning approach to gaussian mixture clustering. In Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings (PART 3 ed., pp. 435-440). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6354 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-15825-4_58

CHEUNG, Yiu Ming ; Jia, Hong. / A cooperative and penalized competitive learning approach to gaussian mixture clustering. Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings. PART 3. ed. 2010. pp. 435-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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title = "A cooperative and penalized competitive learning approach to gaussian mixture clustering",

abstract = "Competitive learning approaches with penalization or cooperation mechanism have been applied to unsupervised data clustering due to their attractive ability of automatic cluster number selection. In this paper, we further investigate the properties of different competitive strategies and propose a novel learning algorithm called Cooperative and Penalized Competitive Learning (CPCL), which implements the cooperation and penalization mechanisms simultaneously in a single competitive learning process. The integration of these two different kinds of competition mechanisms enables the CPCL to have good convergence speed, precision and robustness. Experiments on Gaussian mixture clustering are performed to investigate the proposed algorithm. The promising results demonstrate its superiority.",

author = "CHEUNG, {Yiu Ming} and Hong Jia",

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doi = "10.1007/978-3-642-15825-4_58",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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CHEUNG, YM & Jia, H 2010, A cooperative and penalized competitive learning approach to gaussian mixture clustering. in Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6354 LNCS, pp. 435-440, 20th International Conference on Artificial Neural Networks, ICANN 2010, Thessaloniki, Greece, 15/09/10. https://doi.org/10.1007/978-3-642-15825-4_58

A cooperative and penalized competitive learning approach to gaussian mixture clustering. / CHEUNG, Yiu Ming; Jia, Hong.
Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings. PART 3. ed. 2010. p. 435-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6354 LNCS, No. PART 3).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AB - Competitive learning approaches with penalization or cooperation mechanism have been applied to unsupervised data clustering due to their attractive ability of automatic cluster number selection. In this paper, we further investigate the properties of different competitive strategies and propose a novel learning algorithm called Cooperative and Penalized Competitive Learning (CPCL), which implements the cooperation and penalization mechanisms simultaneously in a single competitive learning process. The integration of these two different kinds of competition mechanisms enables the CPCL to have good convergence speed, precision and robustness. Experiments on Gaussian mixture clustering are performed to investigate the proposed algorithm. The promising results demonstrate its superiority.

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CHEUNG YM, Jia H. A cooperative and penalized competitive learning approach to gaussian mixture clustering. In Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings. PART 3 ed. 2010. p. 435-440. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-15825-4_58

A cooperative and penalized competitive learning approach to gaussian mixture clustering

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