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dc.contributor.authorXiong, Xuejian
dc.contributor.authorTan, Kian Lee
dc.date.accessioned2003-12-13T20:16:37Z
dc.date.available2003-12-13T20:16:37Z
dc.date.issued2004-01
dc.identifier.urihttp://hdl.handle.net/1721.1/3872
dc.description.abstractIn this paper, a similarity-driven cluster merging method is proposed for unsupervised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-driven cluster merging criterion. The similarity between clusters is calculated by a fuzzy cluster similarity matrix, while an adaptive threshold is used for merging. In addition, a modified generalized objective function is used for prototype-based fuzzy clustering. The function includes the p-norm distance measure as well as principal components of the clusters. The number of the principal components is determined automatically from the data being clustered. The performance of this unsupervised fuzzy clustering algorithm is evaluated by several experiments of an artificial data set and a gene expression data set.en
dc.description.sponsorshipSingapore-MIT Alliance (SMA)en
dc.format.extent150799 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesComputer Science (CS);
dc.subjectcluster mergingen
dc.subjectunsupervised fuzzy clusteringen
dc.subjectcluster validityen
dc.subjectgene expression dataen
dc.titleSimilarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clusteringen
dc.typeArticleen


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