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Automatic cluster identification for environnemental applications using the self-organizing maps and a new genetic algorithm / T. Oyana in Geocarto international, vol 25 n° 1 (February 2010)
[article]
Titre : Automatic cluster identification for environnemental applications using the self-organizing maps and a new genetic algorithm Type de document : Article/Communication Auteurs : T. Oyana, Auteur ; D. Dai, Auteur Année de publication : 2010 Article en page(s) : pp 53 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme génétique
[Termes IGN] analyse de groupement
[Termes IGN] carte de Kohonen
[Termes IGN] découverte de connaissances
[Termes IGN] environnement
[Termes IGN] exploration de données géographiques
[Termes IGN] photo-interprétation assistée par ordinateur
[Termes IGN] système d'information géographiqueRésumé : (Auteur) A rapid increase of environmental data dimensionality emphasizes the importance of developing data-driven inductive approaches to geographic analysis. This article uses a loosely coupled strategy to combine the technique of self-organizing maps (SOM) with a new genetic algorithm (GA) for automatic identification of clusters in multidimensional environmental datasets. In the first stage, we employ the well-known classic SOM because it is able to handle the dimensional interactions and capture the number of clusters via visualization; and thus provide extraordinary insights into original data. In the second stage, this new GA rigorously delineates the cluster boundaries using a flexibly oriented elliptical search window. To test this approach, one synthetic and two real-world datasets are employed. The results confirm a more robust and reliable approach that provides a better understanding and interpretation of massive multivariate environmental datasets, thus maximizing our insights. Other key benefits include the fact that it provides a computationally fast and efficient environment to accurately detect clusters, and is highly flexible. In a nutshell, the article presents a computational approach to facilitate knowledge discovery of massive multivariate environmental datasets; as we are too familiar with their accelerating growth rate. Copyright Taylor & Francis Numéro de notice : A2010-054 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040802711687 Date de publication en ligne : 14/04/2009 En ligne : https://doi.org/10.1080/10106040802711687 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30250
in Geocarto international > vol 25 n° 1 (February 2010) . - pp 53 - 69[article]Réservation
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