Temporal and Spatial Data Mining with Second-Order Hidden Markov Models

Jean-François Mari 1 Florence Le Ber 1
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In the frame of designing a knowledge discovery system, we have developed stochastic models based on high-order Hidden Markov Models (HMM2). These models are capable to map sequences of data into a Markov chain in which the transitions between the states depend on the n previous states according to the order of the model. We study the process of achieving information extraction from spatial and temporal data by means of an unsupervised classification. We use therefore a French national database related to land use, named Teruti, which describes the land use at two levels of resolution: the first level is defined by a grid of aerial pictures and the second level is defined by 6x6 matrices of sites located in the pictures. Land use (wheat, corn, forest, ...) is recorded every year on each site. We work with agronomists interested in finding agricultural land use regularities. The temporal segmentation of the data is done by means of second-order HMM that appear to have very good capabilities to locate stationary segments, as shown in our previous work in speech recognition. The spatial classification is performed by defining a fractal scanning of the images with the help of a Hilbert-Peano curve that introduces a total order on the sites, preserving the relation of neighborhood between the sites. We show that the HMM performs a spatial classification that is meaningful for the agronomists. Spatial and temporal classification is achieved simultaneously by means of 2 level HMM2 that measures the \aposteriori probability to map a temporal sequence of images onto a set of hidden states.
Type de document :
Communication dans un congrès
Mohamed Nadif, Amedeo Napoli, Eric San Juan, Alain Sigayret. Fourth International Conference on Knowledge Discovery and Discrete Mathematics - Journées de l'informatique Messine - JIM'2003, Sep 2003, Metz, France, INRIA, pp.247--254, 2003
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Soumis le : mardi 26 septembre 2006 - 09:38:53
Dernière modification le : jeudi 11 janvier 2018 - 06:19:55

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  • HAL Id : inria-00099573, version 1

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Jean-François Mari, Florence Le Ber. Temporal and Spatial Data Mining with Second-Order Hidden Markov Models. Mohamed Nadif, Amedeo Napoli, Eric San Juan, Alain Sigayret. Fourth International Conference on Knowledge Discovery and Discrete Mathematics - Journées de l'informatique Messine - JIM'2003, Sep 2003, Metz, France, INRIA, pp.247--254, 2003. 〈inria-00099573〉

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