Caractérisation d'un parcellaire agricole : comparaison des sacs de noeuds obtenus par un chemin de Hilbert adaptatif et un graphe de voisinage
Identifieur interne : 005788 ( Hal/Corpus ); précédent : 005787; suivant : 005789Caractérisation d'un parcellaire agricole : comparaison des sacs de noeuds obtenus par un chemin de Hilbert adaptatif et un graphe de voisinage
Auteurs : Thomas Guyet ; Sébastien Da Silva ; Claire Lavigne ; Florence Le BerSource :
Abstract
This article focuses on the comparison of approaches for spatial patterns mining. It dealswith agricultural fields, which are mined in two ways, 1) by a fractal linearization methodof space which provides a sequence of fields and 2) by the construction of a neighborhoodgraph. These representations are then used by enumeration algorithms to extract "bags ofnodes" (BoN). A BoN is a vector representing the presence or absence of node types in thesub-structures of sequences or graph. BoNs are compared to highlight the ability of the twoproposed methods to characterize the neighborhood of agricultural fields. The results suggestthat the linearization of space captures most of the information – except some rare elements –about the organization of agricultural fields. Thus, data mining algorithms using these linearrepresentations can be used instead of less efficient tools such as graphs mining algorithms.
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It dealswith agricultural fields, which are mined in two ways, 1) by a fractal linearization methodof space which provides a sequence of fields and 2) by the construction of a neighborhoodgraph. These representations are then used by enumeration algorithms to extract "bags ofnodes" (BoN). A BoN is a vector representing the presence or absence of node types in thesub-structures of sequences or graph. BoNs are compared to highlight the ability of the twoproposed methods to characterize the neighborhood of agricultural fields. The results suggestthat the linearization of space captures most of the information – except some rare elements –about the organization of agricultural fields. Thus, data mining algorithms using these linearrepresentations can be used instead of less efficient tools such as graphs mining algorithms.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="fr">Caractérisation d'un parcellaire agricole : comparaison des sacs de noeuds obtenus par un chemin de Hilbert adaptatif et un graphe de voisinage</title><author role="aut"><persName><forename type="first">Thomas</forename><surname>Guyet</surname></persName><email></email><idno type="idhal">thomas-guyet</idno><idno type="halauthor">471283</idno><affiliation ref="#struct-108028"></affiliation><affiliation ref="#struct-2516"></affiliation></author><author role="aut"><persName><forename type="first">Sébastien</forename><surname>Da Silva</surname></persName><email></email><idno type="halauthor">633764</idno><affiliation ref="#struct-205125"></affiliation><affiliation ref="#struct-37753"></affiliation></author><author role="aut"><persName><forename type="first">Claire</forename><surname>Lavigne</surname></persName><email></email><idno type="halauthor">237160</idno><affiliation ref="#struct-37753"></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">100017</idno><affiliation ref="#struct-217648"></affiliation></author><editor role="depositor"><persName><forename>Florence</forename><surname>Le Ber</surname></persName><email>florence.leber@engees.unistra.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2015-01-06 16:34:38</date><date type="whenModified">2015-10-22 01:08:16</date><date type="whenReleased">2015-01-08 14:59:50</date><date type="whenProduced">2014-01</date><date type="whenEndEmbargoed">2015-01-06</date><ref type="file" target="https://hal.inria.fr/hal-01100583/document"><date notBefore="2015-01-06"></date></ref><ref type="file" subtype="greenPublisher" n="1" target="https://hal.inria.fr/hal-01100583/file/egc_peano_atelier_v1.pdf"><date notBefore="2015-01-06"></date></ref></edition><respStmt><resp>contributor</resp><name key="103335"><persName><forename>Florence</forename><surname>Le Ber</surname></persName><email>florence.leber@engees.unistra.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01100583</idno><idno type="halUri">https://hal.inria.fr/hal-01100583</idno><idno type="halBibtex">guyet:hal-01100583</idno><idno type="halRefHtml">Atelier FST "fouille de données spatiales et temporelles" associé à la conférence EGC, Rennes, 2.. 2014, pp.13</idno><idno type="halRef">Atelier FST "fouille de données spatiales et temporelles" associé à la conférence EGC, Rennes, 2.. 2014, pp.13</idno></publicationStmt><seriesStmt><idno type="stamp" n="AGROCAMPUS-OUEST">Agrocampus Ouest</idno><idno type="stamp" n="UNIV-RENNES1">Université de Rennes 1</idno><idno type="stamp" n="CNRS">CNRS - 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Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="UNIV-UBS">Université de Bretagne Sud</idno><idno type="stamp" n="INSTITUT-TELECOM">Institut Télécom</idno><idno type="stamp" n="SUPELEC">SUPELEC</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="AGROPOLIS">Agropolis</idno><idno type="stamp" n="INRIA2">INRIA 2</idno></seriesStmt><notesStmt><note type="description">Atelier FST "fouille de données spatiales et temporelles" associé à la conférence EGC, Rennes, 28 janvier 2014</note><note type="popular" n="0">No</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="fr">Caractérisation d'un parcellaire agricole : comparaison des sacs de noeuds obtenus par un chemin de Hilbert adaptatif et un graphe de voisinage</title><author role="aut"><persName><forename type="first">Thomas</forename><surname>Guyet</surname></persName><idno type="idHal">thomas-guyet</idno><idno type="halAuthorId">471283</idno><affiliation ref="#struct-108028"></affiliation><affiliation ref="#struct-2516"></affiliation></author><author role="aut"><persName><forename type="first">Sébastien</forename><surname>Da Silva</surname></persName><idno type="halAuthorId">633764</idno><affiliation ref="#struct-205125"></affiliation><affiliation ref="#struct-37753"></affiliation></author><author role="aut"><persName><forename type="first">Claire</forename><surname>Lavigne</surname></persName><idno type="halAuthorId">237160</idno><affiliation ref="#struct-37753"></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">100017</idno><affiliation ref="#struct-217648"></affiliation></author></analytic><monogr><imprint><biblScope unit="pp">13</biblScope><date type="datePub">2014-01</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halDomain" n="sdv.sa.agro">Life Sciences [q-bio]/Agricultural sciences/Agronomy</classCode><classCode scheme="halTypology" n="OTHER">Other publications</classCode></textClass><abstract xml:lang="en">This article focuses on the comparison of approaches for spatial patterns mining. It dealswith agricultural fields, which are mined in two ways, 1) by a fractal linearization methodof space which provides a sequence of fields and 2) by the construction of a neighborhoodgraph. These representations are then used by enumeration algorithms to extract "bags ofnodes" (BoN). A BoN is a vector representing the presence or absence of node types in thesub-structures of sequences or graph. BoNs are compared to highlight the ability of the twoproposed methods to characterize the neighborhood of agricultural fields. The results suggestthat the linearization of space captures most of the information – except some rare elements –about the organization of agricultural fields. Thus, data mining algorithms using these linearrepresentations can be used instead of less efficient tools such as graphs mining algorithms.</abstract><abstract xml:lang="fr">Résumé. Cet article porte sur la comparaison d'approches pour la fouille de motifs spatiaux. Il concerne des données de parcellaires agricoles, qui sont ex-plorées de deux façons, par une méthode de linéarisation de l'espace, qui per-met d'obtenir une séquence, et par la construction d'un graphe de voisinage. Ces représentations sont ensuite exploitées par des algorithmes d'énumération de « sacs de noeuds », c'est-à-dire un vecteur de présence / absence d'un type de noeuds dans les sous-structures de la représentation de l'espace (séquence ou graphe). Les sacs de noeuds sont ensuite comparés pour mettre en évidence la capacité qu'ont les deux méthodes proposées à caractériser le parcellaire agri-cole. Les résultats tendent à montrer que la linéarisation de l'espace capte la majorité de l'information – à l'exception des éléments rares – sur l'organisation du parcellaire agricole et que des outils de fouille de données utilisant ces repré-sentations linéaires peuvent être utilisés à la place d'outils moins efficaces tels que les méthodes de fouille de graphes spatiaux.</abstract><particDesc><org type="consortium">Réseau Payote</org></particDesc></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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