Model choice for binned-EM algorithms of fourteen parsimonious Gaussian mixture models by BIC and ICL criteria

Abstract : Choosing the right model is an important step in model-based clustering approaches. In this framework, BIC and ICL criteria were proposed to choose a model for clustering of standard data. On the other hand, in order to accelerate the data processing when using EM algorithm, this algorithm was adapted to binned data (binned-EM algorithm). Then fourteen binned-EM algorithms of fourteen parsimonious Gaussian mixture models were developed to replace the binned-EM algorithm of the most general Gaussian mixture model when data have a simple structure. So this paper studies the application of BIC and ICL criteria to select a good model which better fits binned data, when clustering is based on these fourteen binned-EM algorithms. Numerical experiments on simulated and real data are performed, and the experimental results are analyzed.
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Contributor : Alexandra Siebert <>
Submitted on : Wednesday, September 25, 2013 - 10:22:38 AM
Last modification on : Thursday, March 29, 2018 - 11:06:05 AM

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Jingwen Wu, Hani Hamdan. Model choice for binned-EM algorithms of fourteen parsimonious Gaussian mixture models by BIC and ICL criteria. 2013 International Conference on System Science and Engineering (ICSSE 2013) , Jul 2013, Budapest, Hungary. pp.351-356, ⟨10.1109/ICSSE.2013.6614690⟩. ⟨hal-00865793⟩

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