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Conference Paper: A tabu search based algorithm for clustering categorical data sets

TitleA tabu search based algorithm for clustering categorical data sets
Authors
Wong, Joyce C.Ng, Michael K.
Issue Date2000
PublisherSpringer.
Citation
Second International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2000), Hong Kong, 13-15 December 2000. In Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents: Second International Conference Shatin, N.T., Hong Kong, China, December 13–15, 2000: Proceedings, 2000, p. 559-564 How to Cite?
DOI: http://dx.doi.org/10.1007/3-540-44491-2_81
Abstract© Springer-Verlag Berlin Heidelberg 2000. 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, we present an algorithm, called tabu search fuzzy k-modes, to extend the fuzzy k-means paradigm to categorical domains. Using the tabu search based technique, our algorithm can explore the solution space beyond local optimality in order to aim at finding a global optimal solution of the fuzzy clustering problem. It is found that our algorithm performs better, in terms of accuracy, than the fuzzy k-modes algorithm.
Persistent Identifierhttp://hdl.handle.net/10722/276493
ISBN
9783540414506
ISSN
0302-9743
2023 SCImago Journal Rankings: 0.606
Series/Report no.Lecture Notes in Computer Science ; 1983

 

DC FieldValueLanguage
dc.contributor.authorWong, Joyce C.-
dc.contributor.authorNg, Michael K.-
dc.date.accessioned2019-09-18T08:33:46Z-
dc.date.available2019-09-18T08:33:46Z-
dc.date.issued2000-
dc.identifier.citationSecond International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2000), Hong Kong, 13-15 December 2000. In Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents: Second International Conference Shatin, N.T., Hong Kong, China, December 13–15, 2000: Proceedings, 2000, p. 559-564-
dc.identifier.isbn9783540414506-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/276493-
dc.description.abstract© Springer-Verlag Berlin Heidelberg 2000. 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, we present an algorithm, called tabu search fuzzy k-modes, to extend the fuzzy k-means paradigm to categorical domains. Using the tabu search based technique, our algorithm can explore the solution space beyond local optimality in order to aim at finding a global optimal solution of the fuzzy clustering problem. It is found that our algorithm performs better, in terms of accuracy, than the fuzzy k-modes algorithm.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofIntelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents: Second International Conference Shatin, N.T., Hong Kong, China, December 13–15, 2000: Proceedings-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 1983-
dc.titleA tabu search based algorithm for clustering categorical data sets-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/3-540-44491-2_81-
dc.identifier.scopuseid_2-s2.0-84929002804-
dc.identifier.spage559-
dc.identifier.epage564-
dc.identifier.eissn1611-3349-
dc.publisher.placeBerlin-
dc.identifier.issnl0302-9743-

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