File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A parallel tabu search heuristic for clustering data sets

TitleA parallel tabu search heuristic for clustering data sets
Authors
Ng, M.
KeywordsCharacter generation
Concurrent computing
Data analysis
Data mining
Fuzzy sets
Testing
Personal communication networks
Mathematics
Clustering methods
Clustering algorithms
Issue Date2003
Citation
Proceedings of the International Conference on Parallel Processing Workshops, 2003, v. 2003-January, p. 230-235 How to Cite?
DOI: http://dx.doi.org/10.1109/ICPPW.2003.1240375
Abstract© 2003 IEEE. 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.
Persistent Identifierhttp://hdl.handle.net/10722/276959
ISSN
1530-2016
2020 SCImago Journal Rankings: 0.211

 

DC FieldValueLanguage
dc.contributor.authorNg, M.-
dc.date.accessioned2019-09-18T08:35:10Z-
dc.date.available2019-09-18T08:35:10Z-
dc.date.issued2003-
dc.identifier.citationProceedings of the International Conference on Parallel Processing Workshops, 2003, v. 2003-January, p. 230-235-
dc.identifier.issn1530-2016-
dc.identifier.urihttp://hdl.handle.net/10722/276959-
dc.description.abstract© 2003 IEEE. 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.-
dc.languageeng-
dc.relation.ispartofProceedings of the International Conference on Parallel Processing Workshops-
dc.subjectCharacter generation-
dc.subjectConcurrent computing-
dc.subjectData analysis-
dc.subjectData mining-
dc.subjectFuzzy sets-
dc.subjectTesting-
dc.subjectPersonal communication networks-
dc.subjectMathematics-
dc.subjectClustering methods-
dc.subjectClustering algorithms-
dc.titleA parallel tabu search heuristic for clustering data sets-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICPPW.2003.1240375-
dc.identifier.scopuseid_2-s2.0-84883403215-
dc.identifier.volume2003-January-
dc.identifier.spage230-
dc.identifier.epage235-
dc.identifier.issnl1530-2016-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats