Scaling-up model-based clustering algorithm by working on clustering features

Huidong JIN; Kwong Sak LEUNG; Man Leung WONG

doi:10.1007/3-540-45675-9_86

Scaling-up model-based clustering algorithm by working on clustering features

Huidong JIN, Kwong Sak LEUNG, Man Leung WONG

Department of Computing and Decision Sciences

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

2 Citations (Scopus)

Abstract

In this paper, we propose EMACF (Expectation- Maximization Algorithm for Clustering Features) to generate clusters from data summaries rather than data items directly. Incorporating with an adaptive grid-based data summarization procedure, we establish a scalable clustering algorithm: gEMACF. The experimental results show that gEMACF can generate more accurate results than other scalable clustering algorithms. The experimental results also indicate that gEMACF can run two order of magnitude faster than the traditional expectation-maximization algorithm with little loss of accuracy.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning — IDEAL 2002 : Third International Conference Manchester, UK, August 12–14, 2002 Proceedings
Editors	Hujun YIN, Nigel ALLINSON, Richard FREEMAN, John KEANE, Simon HUBBARD
Publisher	Springer-Verlag GmbH and Co. KG
Pages	569-575
Number of pages	7
ISBN (Print)	9783540440253
DOIs	https://doi.org/10.1007/3-540-45675-9_86
Publication status	Published - 2002
Event	3rd International Conference on Intelligent Data Engineering and Automated Learning - Manchester, United Kingdom Duration: 12 Aug 2002 → 14 Aug 2002

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	2412
ISSN (Print)	0302-9743

Conference

Conference	3rd International Conference on Intelligent Data Engineering and Automated Learning
Abbreviated title	IDEAL'02
Country/Territory	United Kingdom
City	Manchester
Period	12/08/02 → 14/08/02

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10.1007/3-540-45675-9_86

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JIN, H., LEUNG, K. S., & WONG, M. L. (2002). Scaling-up model-based clustering algorithm by working on clustering features. In H. YIN, N. ALLINSON, R. FREEMAN, J. KEANE, & S. HUBBARD (Eds.), Intelligent Data Engineering and Automated Learning — IDEAL 2002 : Third International Conference Manchester, UK, August 12–14, 2002 Proceedings (pp. 569-575). (Lecture Notes in Computer Science; Vol. 2412). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/3-540-45675-9_86

JIN, Huidong ; LEUNG, Kwong Sak ; WONG, Man Leung. / Scaling-up model-based clustering algorithm by working on clustering features. Intelligent Data Engineering and Automated Learning — IDEAL 2002 : Third International Conference Manchester, UK, August 12–14, 2002 Proceedings. editor / Hujun YIN ; Nigel ALLINSON ; Richard FREEMAN ; John KEANE ; Simon HUBBARD. Springer-Verlag GmbH and Co. KG, 2002. pp. 569-575 (Lecture Notes in Computer Science).

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title = "Scaling-up model-based clustering algorithm by working on clustering features",

abstract = "In this paper, we propose EMACF (Expectation- Maximization Algorithm for Clustering Features) to generate clusters from data summaries rather than data items directly. Incorporating with an adaptive grid-based data summarization procedure, we establish a scalable clustering algorithm: gEMACF. The experimental results show that gEMACF can generate more accurate results than other scalable clustering algorithms. The experimental results also indicate that gEMACF can run two order of magnitude faster than the traditional expectation-maximization algorithm with little loss of accuracy.",

author = "Huidong JIN and LEUNG, {Kwong Sak} and WONG, {Man Leung}",

year = "2002",

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series = "Lecture Notes in Computer Science",

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booktitle = "Intelligent Data Engineering and Automated Learning — IDEAL 2002 : Third International Conference Manchester, UK, August 12–14, 2002 Proceedings",

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note = "3rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL'02 ; Conference date: 12-08-2002 Through 14-08-2002",

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JIN, H, LEUNG, KS & WONG, ML 2002, Scaling-up model-based clustering algorithm by working on clustering features. in H YIN, N ALLINSON, R FREEMAN, J KEANE & S HUBBARD (eds), Intelligent Data Engineering and Automated Learning — IDEAL 2002 : Third International Conference Manchester, UK, August 12–14, 2002 Proceedings. Lecture Notes in Computer Science, vol. 2412, Springer-Verlag GmbH and Co. KG, pp. 569-575, 3rd International Conference on Intelligent Data Engineering and Automated Learning, Manchester, United Kingdom, 12/08/02. https://doi.org/10.1007/3-540-45675-9_86

Scaling-up model-based clustering algorithm by working on clustering features. / JIN, Huidong; LEUNG, Kwong Sak; WONG, Man Leung.
Intelligent Data Engineering and Automated Learning — IDEAL 2002 : Third International Conference Manchester, UK, August 12–14, 2002 Proceedings. ed. / Hujun YIN; Nigel ALLINSON; Richard FREEMAN; John KEANE; Simon HUBBARD. Springer-Verlag GmbH and Co. KG, 2002. p. 569-575 (Lecture Notes in Computer Science; Vol. 2412).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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N2 - In this paper, we propose EMACF (Expectation- Maximization Algorithm for Clustering Features) to generate clusters from data summaries rather than data items directly. Incorporating with an adaptive grid-based data summarization procedure, we establish a scalable clustering algorithm: gEMACF. The experimental results show that gEMACF can generate more accurate results than other scalable clustering algorithms. The experimental results also indicate that gEMACF can run two order of magnitude faster than the traditional expectation-maximization algorithm with little loss of accuracy.

AB - In this paper, we propose EMACF (Expectation- Maximization Algorithm for Clustering Features) to generate clusters from data summaries rather than data items directly. Incorporating with an adaptive grid-based data summarization procedure, we establish a scalable clustering algorithm: gEMACF. The experimental results show that gEMACF can generate more accurate results than other scalable clustering algorithms. The experimental results also indicate that gEMACF can run two order of magnitude faster than the traditional expectation-maximization algorithm with little loss of accuracy.

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JIN H, LEUNG KS, WONG ML. Scaling-up model-based clustering algorithm by working on clustering features. In YIN H, ALLINSON N, FREEMAN R, KEANE J, HUBBARD S, editors, Intelligent Data Engineering and Automated Learning — IDEAL 2002 : Third International Conference Manchester, UK, August 12–14, 2002 Proceedings. Springer-Verlag GmbH and Co. KG. 2002. p. 569-575. (Lecture Notes in Computer Science). Epub 2002 Aug 20. doi: 10.1007/3-540-45675-9_86

Scaling-up model-based clustering algorithm by working on clustering features

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