Clustering Using Monte Carlo Cross-Validation

  title={Clustering Using Monte Carlo Cross-Validation},
  author={Padhraic Smyth},
Finding the “right” number of clusters, Ic, for a data set is a difficult, and often ill-posed, problem. In a probabilistic clustering context, likelihood-ratios, penalized likelihoods, and Bayesian techniques are among the more popular techniques. In this paper a new cross-validated likelihood criterion is investigated for determining cluster structure. A practical clustering algorithm based on Monte Carlo crossvalidation (MCCV) is introduced. The algorithm permits the data analyst to judge if… CONTINUE READING
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