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| HAL : hal-00628247, version 2 |
| Fiche détaillée |  Récupérer au format |
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| Versions disponibles : | v1 (30-09-2011) | v2 (13-10-2012) |
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| Funclust: a curves clustering method using functional random variables density approximation |
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| Julien Jacques 1, 2Cristian Preda 1, 2 |
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| (25/09/2012) |
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| A new method for clustering functional data is proposed under the name Funclust. This method relies on the approximation of the notion of probability density for functional random variables, which generally does not exists. Using the Karhunen-Loeve expansion of a stochastic process, this approximation leads to define an approximation for the density of functional variables. Based on this density approximation, a parametric mixture model is proposed. The parameter estimation is carried out by an EM-like algorithm, and the maximum a posteriori rule provides the clusters. The efficiency of Funclust is illustrated on several real datasets, as well as for the characterization of the Mars surface. |
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| 1 : | Laboratoire Paul Painlevé (LPP) |
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CNRS : UMR8524 – Université Lille I - Sciences et technologies |
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| 2 : | MODAL (INRIA Lille - Nord Europe) |
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INRIA |
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| Domaine | : | Mathématiques/Statistiques Statistiques/Théorie |
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| Functional data – model-based clustering – random variable density – functional principal component analysis |
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| Liste des fichiers attachés à ce document : | |||||
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| hal-00628247, version 2 | |
| http://hal.archives-ouvertes.fr/hal-00628247 | |
| oai:hal.archives-ouvertes.fr:hal-00628247 | |
| Contributeur : Julien Jacques | |
| Soumis le : Samedi 13 Octobre 2012, 10:52:50 | |
| Dernière modification le : Samedi 13 Octobre 2012, 12:55:29 | |
