Monte Carlo Tests of the Accuracy of Cluster Analysis Algorithms: A Comparison of Hierarchical and Nonhierarchical Methods

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Abstract
Nine hierarchical and four nonhierarchical clustering algorithms were compared on their ability to resolve 200 multivariate normal mixtures. The effects of coverage, similarity measures, and cluster overlap were studied by including different levels of coverage for the hierarchical algorithms, Euclidean distances and Pearson correlation coefficients, and truncated multivariate normal mixtures in the analysis. The results confirmed the findings of previous Monte Carlo studies on clustering procedures in that accuracy was inversely related to coverage, and that algorithms using correlation as the similarity measure were significantly more accurate than those using Euclidean distances. No evidence was found for the assumption that the positive effects of the use of correlation coefficients are confined to unconstrained mixture models.

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