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Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper) / Ran Wang in Geoinformatica, vol 20 n° 2 (April - June 2016)  

Public [article]  inGeoinformatica > vol 20 n° 2 (April - June 2016) . - pp 327 - 349 Titre : Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper) Type de document : Article/Communication Auteurs : Ran Wang, Auteur ; Chi-Yin Chow, Auteur ; Yan Lyu, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 327 - 349 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme d'apprentissage [Termes IGN] apprentissage dirigé [Termes IGN] comportement [Termes IGN] données massives [Termes IGN] exploration de données [Termes IGN] histogramme [Termes IGN] point d'intérêt [Termes IGN] positionnement automatique [Termes IGN] téléphonie mobile [Termes IGN] utilisateur Résumé : (auteur) Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method. Numéro de notice : A2016-375 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article DOI : 10.1007/s10707-015-0237-7 En ligne : http://dx.doi.org/10.1007/s10707-015-0237-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81140 [article]