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Article: A method for extension of generative topographic mapping for fuzzy clustering

TitleA method for extension of generative topographic mapping for fuzzy clustering
Authors
KeywordsFuzzy rules
Fuzzy systems
Membership functions
Benchmark datum
Fuzzy c-means clustering
Issue Date2009
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.asis.org/Publications/JASIS/jasis.html
Citation
Journal Of The American Society For Information Science And Technology, 2009, v. 60 n. 2, p. 363-371 How to Cite?
AbstractIn this paper, a new method for fuzzy clustering is proposed that combines generative topographic mapping (GTM) and Fuzzy c-means (FCM) clustering. GTM is used to generate latent variables and their posterior probabilities. These two provide the distribution of the input data in the latent space. FCM determines the seeds of clusters, as well as the resultant clusters and the corresponding membership functions of the input data, based on the latent variables obtained from GTM. Experiments are conducted to compare the results obtained using FCM and the Gustafson-Kessel (GK) algorithm with the proposed method in terms of four cluster-validity indexes. Using simulated and benchmark data sets, it is observed that the hybrid method (GTMFCM) performs better than FCM and GK algorithms in terms of these indexes. It is also found that the superiority of GTMFCM over FCM and GK algorithms becomes more pronounced with the increase in the dimensionality of the input data set.
Persistent Identifierhttp://hdl.handle.net/10722/137111
ISSN
2015 Impact Factor: 2.452
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorBose, Ien_HK
dc.contributor.authorChen, Xen_HK
dc.date.accessioned2011-08-17T09:18:51Z-
dc.date.available2011-08-17T09:18:51Z-
dc.date.issued2009en_HK
dc.identifier.citationJournal Of The American Society For Information Science And Technology, 2009, v. 60 n. 2, p. 363-371en_HK
dc.identifier.issn1532-2882en_HK
dc.identifier.urihttp://hdl.handle.net/10722/137111-
dc.description.abstractIn this paper, a new method for fuzzy clustering is proposed that combines generative topographic mapping (GTM) and Fuzzy c-means (FCM) clustering. GTM is used to generate latent variables and their posterior probabilities. These two provide the distribution of the input data in the latent space. FCM determines the seeds of clusters, as well as the resultant clusters and the corresponding membership functions of the input data, based on the latent variables obtained from GTM. Experiments are conducted to compare the results obtained using FCM and the Gustafson-Kessel (GK) algorithm with the proposed method in terms of four cluster-validity indexes. Using simulated and benchmark data sets, it is observed that the hybrid method (GTMFCM) performs better than FCM and GK algorithms in terms of these indexes. It is also found that the superiority of GTMFCM over FCM and GK algorithms becomes more pronounced with the increase in the dimensionality of the input data set.en_HK
dc.languageeng-
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.asis.org/Publications/JASIS/jasis.htmlen_HK
dc.relation.ispartofJournal of the American Society for Information Science and Technologyen_HK
dc.rightsJournal of the American Society for Information Science and Technology. Copyright © John Wiley & Sons, Inc.-
dc.subjectFuzzy rules-
dc.subjectFuzzy systems-
dc.subjectMembership functions-
dc.subjectBenchmark datum-
dc.subjectFuzzy c-means clustering-
dc.titleA method for extension of generative topographic mapping for fuzzy clusteringen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1532-2882&volume=60&issue=2&spage=363&epage=371&date=2009&atitle=A+method+for+extension+of+generative+topographic+mapping+for+fuzzy+clustering-
dc.identifier.emailBose, I: bose@business.hku.hken_HK
dc.identifier.authorityBose, I=rp01041en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/asi.20974en_HK
dc.identifier.scopuseid_2-s2.0-60549112199en_HK
dc.identifier.hkuros177549-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-60549112199&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume60en_HK
dc.identifier.issue2en_HK
dc.identifier.spage363en_HK
dc.identifier.epage371en_HK
dc.identifier.isiWOS:000263136200012-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridBose, I=7003751502en_HK
dc.identifier.scopusauthoridChen, X=8509885100en_HK

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