A parallel tabu search heuristic for clustering data sets

Michael K Ng

doi:10.1109/ICPPW.2003.1240375

A parallel tabu search heuristic for clustering data sets

Michael K Ng^*

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

4 Citations (Scopus)

Abstract

Clustering methods partition a set of objects into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some defined criteria. In this paper, a parallel tabu search heuristic for solving this problem is developed and implemented on a cluster of PCs. We observe that parallelization does not affect the quality of clustering results, but provides a large saving of the computational times in practice.

Original language	English
Title of host publication	Proceedings - 2003 International Conference on Parallel Processing Workshops, ICPPW 2003
Editors	Chua-Huang Huang, J. Ramanujam
Publisher	IEEE
Pages	230-235
Number of pages	6
ISBN (Electronic)	0769520189
DOIs	https://doi.org/10.1109/ICPPW.2003.1240375
Publication status	Published - Oct 2003
Event	2003 International Conference on Parallel Processing Workshops, ICPPW 2003 - Kaohsiung, Taiwan, Province of China Duration: 6 Oct 2003 → 9 Oct 2003

Publication series

Name	Proceedings of the International Conference on Parallel Processing Workshops
ISSN (Print)	1530-2016

Conference

Conference	2003 International Conference on Parallel Processing Workshops, ICPPW 2003
Country/Territory	Taiwan, Province of China
City	Kaohsiung
Period	6/10/03 → 9/10/03

Scopus Subject Areas

Software
General Mathematics
Hardware and Architecture

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICPPW.2003.1240375

Cite this

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title = "A parallel tabu search heuristic for clustering data sets",

abstract = "Clustering methods partition a set of objects into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some defined criteria. In this paper, a parallel tabu search heuristic for solving this problem is developed and implemented on a cluster of PCs. We observe that parallelization does not affect the quality of clustering results, but provides a large saving of the computational times in practice.",

author = "Ng, {Michael K}",

note = "Research supported in part by RGC Grant Nos. HKU 7130/02P, HKU 7046/03P and HKU CRCG Grant Nos. 10204437, 10203501, and 10203907 Publisher Copyright: {\textcopyright} 2003 IEEE.; 2003 International Conference on Parallel Processing Workshops, ICPPW 2003 ; Conference date: 06-10-2003 Through 09-10-2003",

year = "2003",

month = oct,

doi = "10.1109/ICPPW.2003.1240375",

language = "English",

series = "Proceedings of the International Conference on Parallel Processing Workshops",

publisher = "IEEE",

pages = "230--235",

editor = "Chua-Huang Huang and J. Ramanujam",

booktitle = "Proceedings - 2003 International Conference on Parallel Processing Workshops, ICPPW 2003",

address = "United States",

}

Ng, MK 2003, A parallel tabu search heuristic for clustering data sets. in C-H Huang & J Ramanujam (eds), Proceedings - 2003 International Conference on Parallel Processing Workshops, ICPPW 2003. Proceedings of the International Conference on Parallel Processing Workshops, IEEE, pp. 230-235, 2003 International Conference on Parallel Processing Workshops, ICPPW 2003, Kaohsiung, Taiwan, Province of China, 6/10/03. https://doi.org/10.1109/ICPPW.2003.1240375

A parallel tabu search heuristic for clustering data sets. / Ng, Michael K.
Proceedings - 2003 International Conference on Parallel Processing Workshops, ICPPW 2003. ed. / Chua-Huang Huang; J. Ramanujam. IEEE, 2003. p. 230-235 (Proceedings of the International Conference on Parallel Processing Workshops).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AB - Clustering methods partition a set of objects into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some defined criteria. In this paper, a parallel tabu search heuristic for solving this problem is developed and implemented on a cluster of PCs. We observe that parallelization does not affect the quality of clustering results, but provides a large saving of the computational times in practice.

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BT - Proceedings - 2003 International Conference on Parallel Processing Workshops, ICPPW 2003

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Y2 - 6 October 2003 through 9 October 2003

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A parallel tabu search heuristic for clustering data sets

Abstract

Publication series

Conference

Scopus Subject Areas

UN SDGs

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