Clustering categorical functional data Application to medical discharge letters Medical discharge letters

Vincent Vandewalle 1, 2 Cristian Preda 2, 3
1 CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694
2 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
3 LPP - Laboratoire Paul Painlevé - UMR 8524
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.
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Poster
Working Group on Model-Based Clustering Summer Session: Paris, July 17-23, 2016, Jul 2016, Paris, France. 0010
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Contributeur : Vincent Vandewalle <>
Soumis le : mardi 3 janvier 2017 - 10:47:28
Dernière modification le : mardi 3 juillet 2018 - 11:45:25
Document(s) archivé(s) le : mardi 4 avril 2017 - 13:11:35

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Vincent Vandewalle, Cristian Preda. Clustering categorical functional data Application to medical discharge letters Medical discharge letters. Working Group on Model-Based Clustering Summer Session: Paris, July 17-23, 2016, Jul 2016, Paris, France. 0010. 〈hal-01424950〉

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