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Discovery of spatio-temporal patterns from location based social networks
Béjar Alonso, Javier; Álvarez Napagao, Sergio; Garcia Gasulla, Dario; Gómez Sebastià, Ignasi; Oliva Felipe, Luis Javier; Tejeda Gómez, José Arturo; Vázquez Salceda, Javier
Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic
Location Based Social Networks (LBSN) have become an interesting source for mining user behavior. These networks (e.g. Twitter, Instagram or Foursquare) collect spatio-temporal data from users in a way that they can be seen as a set of collective and distributed sensors on a geographical area. Processing this information in different ways could result in patterns useful for several application domains. These patterns include simple or complex user visits to places in a city or groups of users that can be described by a common behavior. The domains of application range from the recommendation of points of interest to visit and route planning for touristic recommender systems to city analysis and planning. This paper presents the analysis of data collected for several months from such LBSN inside the geographical area of two large cities. The goal is to obtain by means of unsupervised data mining methods sets of patterns that describe groups of users in terms of routes, mobility patterns and behavior profiles that can be useful for city analysis and mobility decisions.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Data mining
Location-based services
Social networks
Spatio temporal data
Clustering
Frequent itemsets
Mineria de dades
Geolocalització, Serveis de
Xarxes socials
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
IOS Press
         

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