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Article: An Algorithm of Fuzzy Collaborative Clustering based on Kernel Competitive Agglomeration

TitleAn Algorithm of Fuzzy Collaborative Clustering based on Kernel Competitive Agglomeration
Authors
Issue Date2013
Citation
Journal of Computers, 2013, v. 8 n. 10, p. 2623-2631 How to Cite?
AbstractKernel-based clustering generally maps the observed data to a high dimensional feature space and can usually achieve preferable classification by enlarging the difference among samples. Competitive kernel clustering creates a competitive environment by means of hierarchical method in which clusters compete for samples based on cardinalities in kernel space. Collaborative clustering implementing on several subsets can be processed by one objective function, which improves the clustering performance by sharing partition matrices among different subsets. In this paper an improved algorithm of collaborative competitive kernel clustering analysis (CCKCA) is proposed, in which the mechanism of collaboration is introduced into competitive kernel clustering. Exploiting the advantages of basic algorithms, CCKCA makes full use of the knowledge of collaborative relation among different subsets based on kernel competitive clustering. The results obtained on the benchmark datasets show that CCKCA can achieve approving clustering performance.
Persistent Identifierhttp://hdl.handle.net/10722/203422

 

DC FieldValueLanguage
dc.contributor.authorGao, CFen_US
dc.contributor.authorWu, XJen_US
dc.contributor.authorYu, PLHen_US
dc.date.accessioned2014-09-19T15:10:26Z-
dc.date.available2014-09-19T15:10:26Z-
dc.date.issued2013en_US
dc.identifier.citationJournal of Computers, 2013, v. 8 n. 10, p. 2623-2631en_US
dc.identifier.urihttp://hdl.handle.net/10722/203422-
dc.description.abstractKernel-based clustering generally maps the observed data to a high dimensional feature space and can usually achieve preferable classification by enlarging the difference among samples. Competitive kernel clustering creates a competitive environment by means of hierarchical method in which clusters compete for samples based on cardinalities in kernel space. Collaborative clustering implementing on several subsets can be processed by one objective function, which improves the clustering performance by sharing partition matrices among different subsets. In this paper an improved algorithm of collaborative competitive kernel clustering analysis (CCKCA) is proposed, in which the mechanism of collaboration is introduced into competitive kernel clustering. Exploiting the advantages of basic algorithms, CCKCA makes full use of the knowledge of collaborative relation among different subsets based on kernel competitive clustering. The results obtained on the benchmark datasets show that CCKCA can achieve approving clustering performance.en_US
dc.languageengen_US
dc.relation.ispartofJournal of Computersen_US
dc.titleAn Algorithm of Fuzzy Collaborative Clustering based on Kernel Competitive Agglomerationen_US
dc.typeArticleen_US
dc.identifier.emailYu, PLH: plhyu@hku.hken_US
dc.identifier.authorityYu, PLH=rp00835en_US
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.4304/jcp.8.10.2623-2631-
dc.identifier.hkuros237265en_US
dc.identifier.volume8en_US
dc.identifier.issue10en_US

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