An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects

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dc.cclicenceCC-BY-NCen
dc.contributor.authorCarmona, C. J.en
dc.contributor.authorGarcia-Vico, A.M.en
dc.contributor.authorMartin, D.en
dc.contributor.authorGarcia-Borroto, M.en
dc.contributor.authordel Jesus, M. J.en
dc.date.acceptance2017-08-28en
dc.date.accessioned2018-07-19T12:12:05Z
dc.date.available2018-07-19T12:12:05Z
dc.date.issued2017-10-18
dc.descriptionopen access articleen
dc.description.abstractEmerging pattern mining is a data mining task that aims to discover discriminative patterns, which can describe emerging behavior with respect to a property of interest. In recent years, the description of datasets has become an interesting field due to the easy acquisition of knowledge by the experts. In this review, we will focus on the descriptive point of view of the task. We collect the existing approaches that have been proposed in the literature and group them together in a taxonomy in order to obtain a general vision of the task. A complete empirical study demonstrates the suitability of the approaches presented. This review also presents future trends and emerging prospects within pattern mining and the benefits of knowledge extracted from emerging patterns.en
dc.exception.reasonThe output was published as gold open accessen
dc.funderSpanish Ministry of Economy and Competitiveness.en
dc.identifier.citationGarcia-Vico, A.M., Carmona, C.J., Martin, D., Garcia-Borroto, M., del Jesus, M. J. (2018) An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects, Wires data mining and knowledge discovery, 8(1), pp. e1231en
dc.identifier.doihttps://doi.org/10.1002/widm.1231
dc.identifier.issn1942-4795
dc.identifier.urihttp://hdl.handle.net/2086/16383
dc.language.isoenen
dc.projectidGrant Number: TIN2015‐68454‐Ren
dc.publisherWileyen
dc.titleAn overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospectsen
dc.typeArticleen

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