Clustering in ordered dissimilarity data
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Timothy C. Havens; James C. Bezdek; James M. Keller; Mihail Popescu
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Article
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2009
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John Wiley and Sons
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English
β 527 KB
This paper presents a new technique for clustering either object or relational data. First, the data are represented as a matrix D of dissimilarity values. D is reordered to D \* using a visual assessment of cluster tendency algorithm. If the data contain clusters, they are suggested by visually app