Temporal and Spatial Data Mining with Second-Order Hidden Markov Models
Identifieur interne : 007577 ( Main/Merge ); précédent : 007576; suivant : 007578Temporal and Spatial Data Mining with Second-Order Hidden Markov Models
Auteurs : Jean-François Mari [France] ; Florence Le Ber [France]Source :
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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.
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region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>données teruti</term><term>fouille de données spatio-temporelle</term><term>temporal and spatial data mining</term><term>teruti data</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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