Caractérisation d'un parcellaire agricole : comparaison des sacs de noeuds obtenus par un chemin de Hilbert adaptatif et un graphe de voisinage
Identifieur interne : 000D17 ( Main/Exploration ); précédent : 000D16; suivant : 000D18Caractérisation d'un parcellaire agricole : comparaison des sacs de noeuds obtenus par un chemin de Hilbert adaptatif et un graphe de voisinage
Auteurs : Thomas Guyet [France] ; Sébastien Da Silva [France] ; Claire Lavigne [France] ; Florence Le Ber [France]Source :
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.
Url:
Affiliations:
- France
- Alsace (région administrative), Grand Est, Lorraine (région)
- Metz, Nancy, Strasbourg
- Université de Lorraine, Université de Strasbourg
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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><affiliations><list><country><li>France</li></country><region><li>Alsace (région administrative)</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li><li>Strasbourg</li></settlement><orgName><li>Université de Lorraine</li><li>Université de Strasbourg</li></orgName></list><tree><country name="France"><noRegion><name sortKey="Guyet, Thomas" sort="Guyet, Thomas" uniqKey="Guyet T" first="Thomas" last="Guyet">Thomas Guyet</name></noRegion><name sortKey="Da Silva, Sebastien" sort="Da Silva, Sebastien" uniqKey="Da Silva S" first="Sébastien" last="Da Silva">Sébastien Da Silva</name><name sortKey="Lavigne, Claire" sort="Lavigne, Claire" uniqKey="Lavigne C" first="Claire" last="Lavigne">Claire Lavigne</name><name sortKey="Le Ber, Florence" sort="Le Ber, Florence" uniqKey="Le Ber F" first="Florence" last="Le Ber">Florence Le Ber</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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