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

Jin, Huidong; Prof. LEUNG Kwong Sak; Wong, Man-Leung

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

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

Author(s)

Jin, Huidong

Prof. LEUNG Kwong Sak

Wong, Man-Leung

Date Issued

2002

Publisher

Springer Verlag

Conference

International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2002)

ISBN

978-354044025-3

ISSN

03029743

Citation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2002, vol. 2412, pp. 569 - 575

DOI

10.1007/3-540-45675-9_86

URI

http://hdl.handle.net/20.500.11861/7646

Type

Conference Paper

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. © Springer-Verlag Berlin Heidelberg 2002.

Subjects

Cluster Algorithm

Mixture Model

Data Item

Gaussian Mixture Mode...

Cluster Accuracy

Options

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

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