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dc.contributor.authorBéjar Alonso, Javier
dc.contributor.authorÁlvarez Napagao, Sergio
dc.contributor.authorGarcia Gasulla, Dario
dc.contributor.authorGómez Sebastià, Ignasi
dc.contributor.authorOliva Felipe, Luis Javier
dc.contributor.authorTejeda Gómez, José Arturo
dc.contributor.authorVázquez Salceda, Javier
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2015-02-20T11:05:45Z
dc.date.created2014
dc.date.issued2014
dc.identifier.citationBéjar, J. [et al.]. Discovery of spatio-temporal patterns from location based social networks. A: International Conference of the Catalan Association for Artificial Intelligence. "Artificial Intelligence Research and Development. Recent Advances and Applications". Barcelona: IOS Press, 2014, p. 126-135.
dc.identifier.isbn978-1-61499-451-0
dc.identifier.urihttp://hdl.handle.net/2117/26445
dc.description.abstractLocation 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.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherIOS Press
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshData mining
dc.subject.lcshLocation-based services
dc.subject.lcshSocial networks
dc.subject.otherSpatio temporal data
dc.subject.otherClustering
dc.subject.otherFrequent itemsets
dc.titleDiscovery of spatio-temporal patterns from location based social networks
dc.typeConference report
dc.subject.lemacMineria de dades
dc.subject.lemacGeolocalització, Serveis de
dc.subject.lemacXarxes socials
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.3233/978-1-61499-452-7-126
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
drac.iddocument15284125
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorBéjar, J.; Alvarez-Napagao, Sergio; Garcia-Gasulla, D.; Gomez, I.; Oliva, L.; Tejeda, J.; Vazquez, J.
upcommons.citation.contributorInternational Conference of the Catalan Association for Artificial Intelligence
upcommons.citation.pubplaceBarcelona
upcommons.citation.publishedtrue
upcommons.citation.publicationNameArtificial Intelligence Research and Development. Recent Advances and Applications
upcommons.citation.startingPage126
upcommons.citation.endingPage135


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