Temporal and spatial data mining with second-order hidden markov models
Identifieur interne : 001B78 ( Istex/Corpus ); précédent : 001B77; suivant : 001B79Temporal and spatial data mining with second-order hidden markov models
Auteurs : J.-F. Mari ; F. Le BerSource :
- Soft Computing [ 1432-7643 ] ; 2006-03-01.
English descriptors
Abstract
Abstract: In the frame of designing a knowledge discovery system, we have developed stochastic models based on high-order hidden Markov models. 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 the land use of a region, named Ter Uti, which describes the land use both in the spatial and temporal domain. Land-use categories (wheat, corn, forest, ...) are logged every year on each site regularly spaced in the region. They constitute a temporal sequence of images in which we look for spatial and temporal dependencies. The temporal segmentation of the data is done by means of a second-order Hidden Markov Model (HMM2) that appears 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 HMM2 performs a classification that is meaningful for the agronomists. Spatial and temporal classification may be achieved simultaneously by means of a two levels HMM2 that measures the a posteriori probability to map a temporal sequence of images onto a set of hidden classes.
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DOI: 10.1007/s00500-005-0501-0
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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 HMM2 performs a classification that is meaningful for the agronomists. 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Le</GivenName><FamilyName>Ber</FamilyName></AuthorName><Contact><Email>fleber@engees.u-strasbg.fr</Email></Contact></Author><Affiliation ID="Aff1"><OrgName>UMR 7503 LORIA</OrgName><OrgAddress><Postbox>B.P. 239</Postbox><Postcode>54506</Postcode><City>Vandœuvre-lès-Nancy</City><Country>(France)</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>CEVH, ENGEES</OrgName><OrgAddress><Street>1 quai Koch</Street><Postcode>67000</Postcode><City>Strasbourg</City><Country>(France)</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>In the frame of designing a knowledge discovery system, we have developed stochastic models based on high-order hidden Markov models. 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 the land use of a region, named Ter Uti, which describes the land use both in the spatial and temporal domain. Land-use categories (wheat, corn, forest, ...) are logged every year on each site regularly spaced in the region. They constitute a temporal sequence of images in which we look for spatial and temporal dependencies.</Para>
<Para>The temporal segmentation of the data is done by means of a second-order Hidden Markov Model (HMM2) that appears 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 HMM2 performs a classification that is meaningful for the agronomists.</Para><Para>Spatial and temporal classification may be achieved simultaneously by means of a two levels HMM2 that measures the a posteriori probability to map a temporal sequence of images onto a set of hidden classes.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Temporal and spatial data mining</Keyword><Keyword>Ter Uti data</Keyword><Keyword>Stochastic models</Keyword><Keyword>Markov chains</Keyword></KeywordGroup></ArticleHeader><NoBody></NoBody></Article></Issue></Volume></Journal></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Temporal and spatial data mining with second-order hidden markov models</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Temporal and spatial data mining with second-order hidden markov models</title></titleInfo><name type="personal" displayLabel="corresp"><namePart type="given">J.-F.</namePart><namePart type="family">Mari</namePart><affiliation>UMR 7503 LORIA, B.P. 239, 54506, Vandœuvre-lès-Nancy, (France)</affiliation><affiliation>E-mail: jfmari@loria.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">F. Le</namePart><namePart type="family">Ber</namePart><affiliation>CEVH, ENGEES, 1 quai Koch, 67000, Strasbourg, (France)</affiliation><affiliation>E-mail: fleber@engees.u-strasbg.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</genre><originInfo><publisher>Springer-Verlag</publisher><place><placeTerm type="text">Berlin/Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2006-03-01</dateIssued><copyrightDate encoding="w3cdtf">2005</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: In the frame of designing a knowledge discovery system, we have developed stochastic models based on high-order hidden Markov models. 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 the land use of a region, named Ter Uti, which describes the land use both in the spatial and temporal domain. Land-use categories (wheat, corn, forest, ...) are logged every year on each site regularly spaced in the region. They constitute a temporal sequence of images in which we look for spatial and temporal dependencies. The temporal segmentation of the data is done by means of a second-order Hidden Markov Model (HMM2) that appears 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 HMM2 performs a classification that is meaningful for the agronomists. Spatial and temporal classification may be achieved simultaneously by means of a two levels HMM2 that measures the a posteriori probability to map a temporal sequence of images onto a set of hidden classes.</abstract><note>Focus</note><subject lang="en"><genre>Keywords</genre><topic>Temporal and spatial data mining</topic><topic>Ter Uti data</topic><topic>Stochastic models</topic><topic>Markov chains</topic></subject><relatedItem type="host"><titleInfo><title>Soft Computing</title><subTitle>A Fusion of Foundations, Methodologies and Applications</subTitle></titleInfo><titleInfo type="abbreviated"><title>Soft Comput</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><originInfo><publisher>Springer</publisher><dateIssued encoding="w3cdtf">2006-03-01</dateIssued><copyrightDate encoding="w3cdtf">2006</copyrightDate></originInfo><subject><genre>Engineering</genre><topic>Theory of Computation</topic><topic>Computing Methodologies</topic><topic>Mathematical Logic and Foundations</topic><topic>Numerical and Computational Methods in Engineering</topic><topic>Control Engineering</topic></subject><identifier type="ISSN">1432-7643</identifier><identifier type="eISSN">1433-7479</identifier><identifier type="JournalID">500</identifier><identifier type="JournalSPIN">30305082</identifier><identifier type="VolumeIssueCount">8</identifier><part><date>2006</date><detail type="volume"><number>10</number><caption>vol.</caption></detail><detail type="issue"><number>5</number><caption>no.</caption></detail><extent unit="pages"><start>406</start><end>414</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2006</recordOrigin></recordInfo></relatedItem><identifier type="istex">78300E31C012C8D21206A8F49C5E5D27DDA6F7A2</identifier><identifier type="ark">ark:/67375/VQC-T3K3K7RF-0</identifier><identifier type="DOI">10.1007/s00500-005-0501-0</identifier><identifier type="ArticleID">501</identifier><identifier type="ArticleID">Art1</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg, 2005</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-3XSW68JL-F">springer</recordContentSource><recordOrigin>Springer-Verlag Berlin Heidelberg, 2005</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/VQC-T3K3K7RF-0/record.json</uri></json:item></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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|texte= Temporal and spatial data mining with second-order hidden markov models
}}
| This area was generated with Dilib version V0.6.33. |  |