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Article: Agglomerative fuzzy K-Means clustering algorithm with selection of number of clusters

TitleAgglomerative fuzzy K-Means clustering algorithm with selection of number of clusters
Authors
KeywordsCluster validation
Number of clusters
Fuzzy K-Means clustering
Agglomerative
Issue Date2008
Citation
IEEE Transactions on Knowledge and Data Engineering, 2008, v. 20, n. 11, p. 1519-1534 How to Cite?
AbstractIn this paper, we present an agglomerative fuzzy K-Means clustering algorithm for numerical data, an extension to the standard fuzzy K-Means algorithm by introducing a penalty term to the objective function to make the clustering process not sensitive to the Initial cluster centers. The new algorithm can produce more consistent clustering results from different sets of Initial clusters centers. Combined with cluster validation techniques, the new algorithm can determine the number of clusters In a data set, which is a well-known problem In K-Means clustering. Experimental results on synthetic data sets (2 to 5 dimensions, 500 to 5,000 objects and 3 to 7 clusters), the BIRCH two-dimensional data set of 20,000 objects and 100 cluster0and the WINE data set of 178 objects, 17 dimensions, and 3 clusters from UCI have demonstrated the effectiveness of the new algorithm in producing consistent clustering results and determining the correct number of clusters in different data sets, some with overlapping inherent clusters. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/276829
ISSN
2023 Impact Factor: 8.9
2023 SCImago Journal Rankings: 2.867
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Mark Junjie-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorCheung, Yiu Ming-
dc.contributor.authorHuang, Joshua Zhexue-
dc.date.accessioned2019-09-18T08:34:47Z-
dc.date.available2019-09-18T08:34:47Z-
dc.date.issued2008-
dc.identifier.citationIEEE Transactions on Knowledge and Data Engineering, 2008, v. 20, n. 11, p. 1519-1534-
dc.identifier.issn1041-4347-
dc.identifier.urihttp://hdl.handle.net/10722/276829-
dc.description.abstractIn this paper, we present an agglomerative fuzzy K-Means clustering algorithm for numerical data, an extension to the standard fuzzy K-Means algorithm by introducing a penalty term to the objective function to make the clustering process not sensitive to the Initial cluster centers. The new algorithm can produce more consistent clustering results from different sets of Initial clusters centers. Combined with cluster validation techniques, the new algorithm can determine the number of clusters In a data set, which is a well-known problem In K-Means clustering. Experimental results on synthetic data sets (2 to 5 dimensions, 500 to 5,000 objects and 3 to 7 clusters), the BIRCH two-dimensional data set of 20,000 objects and 100 cluster0and the WINE data set of 178 objects, 17 dimensions, and 3 clusters from UCI have demonstrated the effectiveness of the new algorithm in producing consistent clustering results and determining the correct number of clusters in different data sets, some with overlapping inherent clusters. © 2008 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineering-
dc.subjectCluster validation-
dc.subjectNumber of clusters-
dc.subjectFuzzy K-Means clustering-
dc.subjectAgglomerative-
dc.titleAgglomerative fuzzy K-Means clustering algorithm with selection of number of clusters-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TKDE.2008.88-
dc.identifier.scopuseid_2-s2.0-52949101047-
dc.identifier.volume20-
dc.identifier.issue11-
dc.identifier.spage1519-
dc.identifier.epage1534-
dc.identifier.isiWOS:000259259600006-
dc.identifier.issnl1041-4347-

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