A Nonparametric Statistical Approach to Clustering via Mode Identification
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Inference for multivariate normal mixtures
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The main result of this article states that one can get as many as D+1 modes from just a two component normal mixture in D dimensions. Multivariate mixture models are widely used for modeling homogeneous populations and for cluster analysis. Either the components directly or modes arising from these components are often used to extract individual clusters. Although in lower dimensions these strategies work well, our results show that high dimensional mixtures are often very complex and researchers should take extra precautions when using mixture models for cluster analysis. Further our analysis shows that the number of modes depends on the component means and eigenvalues of the ratio of the two component covariance matrices, which in turn provides a clear guideline as to when one can use mixture analysis for clustering high dimensional data.