Temporal and Spatial Data Mining with Second-Order Hidden Markov Models
Identifieur interne : 004A34 ( Hal/Curation ); précédent : 004A33; suivant : 004A35Temporal 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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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><hal api="V3"><titleStmt><title xml:lang="en">Temporal and Spatial Data Mining with Second-Order Hidden Markov Models</title><author role="aut"><persName><forename type="first">Jean-François</forename><surname>Mari</surname></persName><email>jfmari@loria.fr</email><idno type="halauthor">129884</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2358"></affiliation></author><author role="aut"><persName><forename type="first">Florence</forename><surname>Le Ber</surname></persName><email></email><idno type="idhal">florence-le-ber</idno><idno type="halauthor">130108</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2358"></affiliation></author><editor role="depositor"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-09-26 09:38:53</date><date type="whenModified">2016-05-18 08:59:33</date><date type="whenReleased">2006-09-28 15:22:46</date><date type="whenProduced">2003-09</date></edition><respStmt><resp>contributor</resp><name key="108626"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00099573</idno><idno type="halUri">https://hal.inria.fr/inria-00099573</idno><idno type="halBibtex">mari:inria-00099573</idno><idno type="halRefHtml">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</idno><idno type="halRef">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</idno></publicationStmt><seriesStmt><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="commentary">Colloque avec actes sans comité de lecture. internationale.</note><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Temporal and Spatial Data Mining with Second-Order Hidden Markov Models</title><author role="aut"><persName><forename type="first">Jean-François</forename><surname>Mari</surname></persName><email>jfmari@loria.fr</email><idno type="halAuthorId">129884</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2358"></affiliation></author><author role="aut"><persName><forename type="first">Florence</forename><surname>Le Ber</surname></persName><idno type="idHal">florence-le-ber</idno><idno type="halAuthorId">130108</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2358"></affiliation></author></analytic><monogr><idno type="localRef">A03-R-238 || mari03a</idno><meeting><title>Fourth International Conference on Knowledge Discovery and Discrete Mathematics - Journées de l'informatique Messine - JIM'2003</title><date type="start">2003-09</date><settlement>Metz, France</settlement></meeting><respStmt><resp>conferenceOrganizer</resp><name>IUT de Metz, LITA</name></respStmt><editor>Mohamed Nadif, Amedeo Napoli, Eric San Juan, Alain Sigayret</editor><imprint><publisher>INRIA</publisher><biblScope unit="pp">247--254</biblScope><date type="datePub">2003-09</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">fouille de données spatio-temporelle</term><term xml:lang="fr">teruti data</term><term xml:lang="fr">temporal and spatial data mining</term><term xml:lang="fr">données teruti</term></keywords><classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><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.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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