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An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering / Xiaojing Wu in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1822 - 1848 Titre : An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering Type de document : Article/Communication Auteurs : Xiaojing Wu, Auteur ; Changxiu Cheng, Auteur ; Raul Zurita-Milla, Auteur Année de publication : 2020 Article en page(s) : pp 1822 - 1848 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par nuées dynamiques [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] géoréférencement [Termes descripteurs IGN] modélisation spatio-temporelle [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] taxinomie Résumé : (auteur) Even though many studies have shown the usefulness of clustering for the exploration of spatio-temporal patterns, until now there is no systematic description of clustering methods for geo-referenced time series (GTS) classified as one-way clustering, co-clustering and tri-clustering methods. Moreover, the selection of a suitable clustering method for a given dataset and task remains to be a challenge. Therefore, we present an overview of existing clustering methods for GTS, using the aforementioned classification, and compare different methods to provide suggestions for the selection of appropriate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the clustering methods by using representative algorithms and a case study dataset. Our results indicate that tri-clustering methods are more powerful in exploring complex patterns at the cost of additional computational effort, whereas one-way clustering and co-clustering methods yield less complex patterns and require less running time. However, the selection of the most suitable method should depend on the data type, research questions, computational complexity, and the availability of the methods. Finally, the described classification can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns. Numéro de notice : A2020-477 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1726922 date de publication en ligne : 16/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1726922 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95624 [article]

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