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dc.contributor.authorSanchez-Hernandez, Germanen
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorAgell, Nuriaen
dc.contributor.authorAguado, Juan Carlosen
dc.date.accessioned2013-04-03T08:58:08Z
dc.date.available2013-04-03T08:58:08Z
dc.date.issued2013-05
dc.identifier.citationSanchez-Hernandez, G. et al. (2013) Ranking and selection of unsupervised learning marketing segmentation. Knowledge-Based Systems, 44, pp. 20–33en
dc.identifier.urihttp://hdl.handle.net/2086/8319
dc.descriptionThis research paper has been partially conducted during a three-months visiting period by German Sanchez-Hernandez to the Centre for Computational Intelligence (CCI).en
dc.descriptionNOTICE: this is the author’s version of a work that was accepted for publication in <Journal title>. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systems, 44, pp. 20–33 http://dx.doi.org/10.1016/j.knosys.2013.01.012
dc.description.abstractThis paper addresses the problem of choosing the most appropriate classification from a given set of classifications of a set of patterns. This is a relevant topic on unsupervised systems and clustering analysis because different classifications can in general be obtained from the same data set. The provided methodology is based on five fuzzy criteria which are aggregated using an Ordered Weighted Averaging (OWA) operator. To this end, a novel multi-criteria decision making (MCDM) system is defined, which assesses the degree up to which each criterion is met by all classifications. The corresponding single evaluations are then proposed to be aggregated into a collective one by means of an OWA operator guided by a fuzzy linguistic quantifier, which is used to implement the concept of fuzzy majority in the selection process. This new methodology is applied to a real marketing case based on a business to business (B2B) environment to help marketing experts during the segmentation process. As a result, a segmentation containing three segments consisting of 35, 98 and 127 points of sale respectively is selected to be the most suitable to endorse marketing strategies of the firm. Finally, an analysis of the managerial implications of the proposed methodology solution is provided.en
dc.description.sponsorshipThis work is supported by the SENSORIAL Research Project (TIN2010-20966- C02-01, 02), funded by the Spanish Ministry of Science and Information Technology.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectfuzzy selection criteriaen
dc.subjectOWA operatoren
dc.subjectclassification selectionen
dc.subjectmarket segmentationen
dc.subjectlinguistic quantifieren
dc.titleRanking and selection of unsupervised learning marketing segmentationen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.knosys.2013.01.012
dc.researchgroupDIGITSen
dc.researchgroupCentre for Computational Intelligence
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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