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Erschienen in:
2008 | OriginalPaper | Buchkapitel
VCV2 – Visual Cluster Validity
verfasst von : Jacalyn M. Huband, James C. Bezdek
Erschienen in: Computational Intelligence: Research Frontiers
Verlag: Springer Berlin Heidelberg
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All clustering algorithms partition data into a specified or algorithmically determined number of clusters, whether or not that number of clusters actually exists in the data. Therefore, identifying a "best" solution amongst a set of candidate partitions is an important step in the clustering process. This paper presents a visual technique for comparing found partitions with a pre-clustering VAT (Visual Assessment of cluster Tendency) image of the unlabeled input data. The method is developed independent of any particular clustering algorithm, and then illustrated with numerical examples that use the fuzzy c-means clustering method. The experiments use samples from mixtures of bivariate normals, a bivariate uniform, and a small real data set to illustrate the efficacy of the method.