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)

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 publicationIntelligent Data Engineering and Automated Learning — IDEAL 2002 : Third International Conference Manchester, UK, August 12–14, 2002 Proceedings
EditorsHujun YIN, Nigel ALLINSON, Richard FREEMAN, John KEANE, Simon HUBBARD
PublisherSpringer-Verlag GmbH and Co. KG
Pages569-575
Number of pages7
ISBN (Print)9783540440253
DOIs
Publication statusPublished - 2002
Event3rd International Conference on Intelligent Data Engineering and Automated Learning - Manchester, United Kingdom
Duration: 12 Aug 200214 Aug 2002

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume2412
ISSN (Print)0302-9743

Conference

Conference3rd International Conference on Intelligent Data Engineering and Automated Learning
Abbreviated titleIDEAL'02
CountryUnited Kingdom
CityManchester
Period12/08/0214/08/02

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  • Cite this

    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