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

Huidong JIN, Kwong Sak LEUNG, Man Leung WONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-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 languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer-Verlag GmbH and Co. KG
Pages569-575
Number of pages7
Volume2412
ISBN (Print)9783540440253
DOIs
Publication statusPublished - 1 Jan 2002

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Clustering algorithms

Bibliographical note

Paper presented at the 3rd International Conference on Intelligent Data Engineering and Automated Learning, Aug 12-14, 2002, Manchester, England. ISBN of the source publication: 9783540440253

Cite this

JIN, H., LEUNG, K. S., & WONG, M. L. (2002). Scaling-up model-based clustering algorithm by working on clustering features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2412, pp. 569-575). 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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2412 Springer-Verlag GmbH and Co. KG, 2002. pp. 569-575
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JIN, H, LEUNG, KS & WONG, ML 2002, Scaling-up model-based clustering algorithm by working on clustering features. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2412, Springer-Verlag GmbH and Co. KG, pp. 569-575. 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.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2412 Springer-Verlag GmbH and Co. KG, 2002. p. 569-575.

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

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JIN H, LEUNG KS, WONG ML. Scaling-up model-based clustering algorithm by working on clustering features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2412. Springer-Verlag GmbH and Co. KG. 2002. p. 569-575 https://doi.org/10.1007/3-540-45675-9_86