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Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees

Clustering is important in many fields including manufacturing, biology, finance, and astronomy. Mixture models are a popular approach due to their statistical foundations, and EM is a very popular method for finding mixture models. EM, however, requires many accesses of the data, and thus has been dismissed as impractical (e.g. [9]) for data mining of enormous datasets. We present a new algorithm, based on the multiresolution kd-trees of [5], which dramatically reduces the cost of EM-based clustering, with savings rising linearly with the number of datapoints. Although presented here for maximum likelihood estimation of Gaussian mixture models, it is also applicable to non-Gaussian models (provided class densities are monotonic in Mahalanobis distance), mixed categorical/ numeric clusters. and Bayesian methods such as Antoclass [1].