Clustering categorical functional data Application to medical discharge letters

Vincent Vandewalle 1, 2 Cristina Cozma 1 Cristian Preda 3, 1
1 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille, Université de Lille 1, IUT’A
Abstract : Categorical functional data represented by paths of a stochastic jump process are considered for clustering. For paths of the same length, the extension of the multiple correspondence analysis allows the use of well-known methods for clustering finite dimensional data. When the paths are of different lengths, the analysis is more complex. In this case, for Markov models we propose an EM algorithm to estimate a mixture of Markov processes. A simulation study as well as a real application on hospital stays will be presented.
Type de document :
Communication dans un congrès
8th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2015, Londres, France. 2015
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https://hal.inria.fr/hal-01251284
Contributeur : Vincent Vandewalle <>
Soumis le : mardi 5 janvier 2016 - 21:38:27
Dernière modification le : mercredi 25 avril 2018 - 14:23:16

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Vincent Vandewalle, Cristina Cozma, Cristian Preda. Clustering categorical functional data Application to medical discharge letters. 8th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2015, Londres, France. 2015. 〈hal-01251284〉

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