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dc.contributor.authorPalomino Gayete, Arturo
dc.contributor.authorGibert, Karina
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2015-05-14T11:34:29Z
dc.date.created2014
dc.date.issued2014
dc.identifier.citationPalomino, A.; Gibert, Karina. Web pattern detection for business intelligence with data mining. A: International Conference of the Catalan Association for Artificial Intelligence. "Artificial Intelligence Research and Development. Recent Advances and Applications". Barcelona: IOS Press, 2014, p. 277-280.
dc.identifier.isbn978-1-61499-451-0
dc.identifier.urihttp://hdl.handle.net/2117/27913
dc.description.abstractFinding Internet browsing patterns is a current hot topic, with expected benefits in many areas, marketing and business intelligence among others. Discovering user's internet habits might improve fields like chained-publicity, e-commerce and media optimization. The large amount of data contained in log files that is currently being analyzed to find user's patterns require efficient and scalable data mining solutions. This paper proposes an algorithm to identify the most frequent route followed by Internet users, based on a specific combination of simple statistical and vectorial operators that provides exact solution with a really cheap computational cost. In the paper, the performance is compared with other two algorithms and an application to a real case study in the field of bussiness intelligence and chained publicity is presented.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherIOS Press
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica::Modelització matemàtica
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Programació matemàtica
dc.subject.lcshNumerical analysis--Simulation methods
dc.subject.lcshNumerical analysis
dc.subject.othercross media
dc.subject.otherdata mining
dc.subject.otherEclat
dc.subject.otherinternet
dc.subject.othermarketing mix
dc.subject.othermedia optimization
dc.subject.otherreturn of investment
dc.subject.otherweb domain
dc.subject.otherweb mining
dc.titleWeb pattern detection for business intelligence with data mining
dc.typeConference report
dc.subject.lemacAnàlisi numèrica
dc.subject.lemacAnàlisi numèrica -- Programació
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.3233/978-1-61499-452-7-277
dc.description.peerreviewedPeer Reviewed
dc.subject.ams65K Mathematical programming, optimization and variational techniques
dc.subject.amsClassificació AMS::65 Numerical analysis::65C Probabilistic methods, simulation and stochastic differential equations
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac15286261
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorPalomino, A.; Gibert, Karina
local.citation.contributorInternational Conference of the Catalan Association for Artificial Intelligence
local.citation.pubplaceBarcelona
local.citation.publicationNameArtificial Intelligence Research and Development. Recent Advances and Applications
local.citation.startingPage277
local.citation.endingPage280


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