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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
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
2 METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694
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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https://hal.inria.fr/hal-01251284
Contributor : Vincent Vandewalle <>
Submitted on : Tuesday, January 5, 2016 - 9:38:27 PM
Last modification on : Tuesday, December 8, 2020 - 9:44:55 AM

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  • HAL Id : hal-01251284, version 1

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CNRS | INSMI | INRIA | UNIV-LILLE

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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. ⟨hal-01251284⟩

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