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Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clustering

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dc.contributor.author Xiong, Xuejian
dc.contributor.author Tan, Kian Lee
dc.date.accessioned 2003-12-13T20:16:37Z
dc.date.available 2003-12-13T20:16:37Z
dc.date.issued 2004-01
dc.identifier.uri http://hdl.handle.net/1721.1/3872
dc.description.abstract In 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.sponsorship Singapore-MIT Alliance (SMA) en
dc.format.extent 150799 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartofseries Computer Science (CS);
dc.subject cluster merging en
dc.subject unsupervised fuzzy clustering en
dc.subject cluster validity en
dc.subject gene expression data en
dc.title Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clustering en
dc.type Article en


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