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Conference papers

Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study

Md Abul Hasnat 1 Julien Velcin 1 Stéphane Bonnevay 1 Julien Jacques 1, 2
2 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : In this paper, we study different discrete data clustering methods, which use the Model-Based Clustering (MBC) framework with the Multinomial distribution. Our study comprises several relevant issues, such as initialization, model estimation and model selection. Additionally, we propose a novel MBC method by efficiently combining the partitional and hierarchical clustering techniques. We conduct experiments on both synthetic and real data and evaluate the methods using accuracy, stability and computation time. Our study identifies appropriate strategies to be used for discrete data analysis with the MBC methods. Moreover, our proposed method is very competitive w.r.t. clustering accuracy and better w.r.t. stability and computation time.
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Submitted on : Thursday, September 24, 2015 - 8:38:37 AM
Last modification on : Friday, April 15, 2022 - 3:00:01 PM
Long-term archiving on: : Tuesday, December 29, 2015 - 9:34:44 AM


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  • HAL Id : hal-01203561, version 1



Md Abul Hasnat, Julien Velcin, Stéphane Bonnevay, Julien Jacques. Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study. Intelligent Data Analysis, Oct 2015, Saint Etienne, France. ⟨hal-01203561⟩



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