A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Very fast EM-based mixture model clustering using multiresolution kd-trees
Proceedings of the 1998 conference on Advances in neural information processing systems II
Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data
Machine Learning - Special issue: Unsupervised learning
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Mixture-model-based clustering has become a popular approach in many data analysis problems for its statistical properties and the implementation simplicity of the EM algorithm. However the computation time of the EM algorithm and its variants increases significantly with the sample size. For large data sets, performing clustering on grouped data constitutes an efficient alternative to speed up the algorithms execution time. A rapid and effective algorithm dedicated to grouped data clustering is then proposed in this paper. Inspired by the Classification EM algorithm (CEM), the proposed approach estimates the missing sample at each iteration. An experimental study using simulated data and real acoustic emission data in the context of a flaw detection application on gas tanks reveals good performances of the proposed approach in terms of partitioning precision and computing time.