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I was interested in this technique but I haven't found any reference, searching Google, Google Scholar. We need a source/reference.[[User:Zorgkang|Moo]] ([[User talk:Zorgkang|talk]]) 20:25, 11 May 2012 (UTC)
:I fcound the following on what appears yp be an old copy of the artcle [[cluster analysis]] at http://biocomp.bioen.uiuc.edu/oscar/tools/Hierarchical_Clustering.html
:::V-means clustering
:::V-means clustering utilizes cluster analysis and nonparametric statistical tests to key researchers into segments of data that may contain distinct homogenous sub-sets. The methodology embraced by V-means clustering circumvents many of the problems that traditionally beleaguer standard techniques for categorizing data. First, instead of relying on analyst predictions for the number of distinct sub-sets (k-means clustering), V-means clustering generates a pareto optimal number of sub-sets. V-means clustering is calibrated to a user-defined confidence level p, whereby the algorithm divides the data and then recombines the resulting groups until the probability that any given group belongs to the same distribution as either of its neighbors is less than p.
:::Second, V-means clustering makes use of repeated iterations of the nonparametric Kolmogorov-Smirnov test. Standard methods of dividing data into its constituent parts are often entangled in definitions of distances (distance measure clustering) or in assumptions about the normality of the data (expectation maximization clustering), but nonparametric analysis draws inference from the distribution functions of sets.
:::Third, the method is conceptually simple. Some methods combine multiple techniques in sequence in order to produce more robust results. From a practical standpoint this muddles the meaning of the results and frequently leads to conclusions typical of “data dredging.”
:Unfortunately there was no citation. [[User:Melcombe|Melcombe]] ([[User talk:Melcombe|talk]]) 22:27, 11 May 2012 (UTC)
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