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dc.contributor.authorLezcano Ríos, Christian Gerardo
dc.contributor.authorArias Vicente, Marta
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2020-07-10T08:42:30Z
dc.date.available2020-07-10T08:42:30Z
dc.date.issued2019
dc.identifier.citationLezcano, C.; Arias, M. Characterizing transactional databases for frequent itemset mining. A: SIAM International Conference on Data Mining. "Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning: co-located with SIAM International Conference on Data Mining (SDM 2019), Calgary, Alberta, Canada, May 4th, 2019". CEUR-WS.org, 2019, p. 44-53.
dc.identifier.isbn1613-0073
dc.identifier.urihttp://hdl.handle.net/2117/192797
dc.description.abstractThis paper presents a study of the characteristics of transactional databases used in frequent itemset mining. Such characterizations have typically been used to benchmark and understand the data mining algorithms working on these databases. The aim of our study is to give a picture of how diverse and representative these benchmarking databases are, both in general but also in the context of particular empirical studies found in the literature. Our proposed list of metrics contains many of the existing metrics found in the literature, as well as new ones. Our study shows that our list of metrics is able to capture much of the datasets’ inner complexity and thus provides a good basis for the characterization of transactional datasets. Finally, we provide a set of representative datasets based on our characterization that may be used as a benchmark safely.
dc.description.sponsorshipBoth authors have been partially supported by TIN2017-89244-R from MINECO (Spain’s Ministerio de Economia, Industria y Competitividad) and the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya). Christian Lezcano is supported by Paraguay’s Foreign Postgraduate Scholarship Programme Don Carlos Antonio López (BECAL).
dc.format.extent10 p.
dc.language.isoeng
dc.publisherCEUR-WS.org
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshData mining
dc.subject.lcshDatabases
dc.subject.otherData characterization
dc.subject.otherTransactional databases
dc.subject.otherFrequent itemset mining
dc.titleCharacterizing transactional databases for frequent itemset mining
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacMineria de dades
dc.subject.lemacBases de dades
dc.contributor.groupUniversitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ceur-ws.org/Vol-2436/article_5.pdf
dc.rights.accessOpen Access
local.identifier.drac28854933
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/TIN2017-89244-R
local.citation.authorLezcano, C.; Arias, M.
local.citation.contributorSIAM International Conference on Data Mining
local.citation.publicationNameProceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning: co-located with SIAM International Conference on Data Mining (SDM 2019), Calgary, Alberta, Canada, May 4th, 2019
local.citation.startingPage44
local.citation.endingPage53


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