Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7646
Title: | Scaling-up model-based clustering algorithm by working on clustering features |
Authors: | Jin, Huidong Prof. LEUNG Kwong Sak Wong, Man-Leung |
Issue Date: | 2002 |
Publisher: | Springer Verlag |
Source: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2002, vol. 2412, pp. 569 - 575 |
Journal: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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. |
Type: | Conference Paper |
URI: | http://hdl.handle.net/20.500.11861/7646 |
ISBN: | 978-354044025-3 |
ISSN: | 03029743 |
DOI: | 10.1007/3-540-45675-9_86 |
Appears in Collections: | Applied Data Science - Publication |
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