From indoor paths to gender prediction with soft clustering

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dc.authorscopusid57202924825
dc.authorscopusid8572344300
dc.contributor.authorDoğan, Onur
dc.contributor.authorÖztayşi, Başar
dc.date.accessioned2022-02-15T16:57:33Z
dc.date.available2022-02-15T16:57:33Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractCustomer-based practices enable benefits to organizations in a contentious business. Offering individualized proposals increase customer loyalty to be able to afloat. Understanding customers is a vital difficulty to perform personalized recommendations. As a demographic feature, gender information essentially cannot be captured by human tracking technologies. Hence, several procedures are improved to predict undiscovered gender information. In the research, the followed indoor paths in a shopping mall are used to predict customer genders using fuzzy c-medoids, one of the soft clustering techniques. A Levenshtein-based fuzzy classification methodology is proposed the followed paths as string data. Although some studies focused on gender prediction, no research has centered on path-oriented. The novelty of the investigation is to analyze customer path data for the gender classes.en_US
dc.description.sponsorshipResearch Fund of the Istanbul Technical UniversityIstanbul Technical University [MGA-2019-41949]en_US
dc.description.sponsorshipThis work was supported by Research Fund of the Istanbul Technical University. Project Number: MGA-2019-41949en_US
dc.identifier.doi10.3233/JIFS-189116
dc.identifier.endpage6538en_US
dc.identifier.issn1064-1246
dc.identifier.issn1875-8967
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85096955754en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage6529en_US
dc.identifier.urihttps://doi.org/10.3233/JIFS-189116
dc.identifier.urihttps://hdl.handle.net/20.500.14034/203
dc.identifier.volume39en_US
dc.identifier.wosWOS:000595520600046en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIOS Pressen_US
dc.relation.journalJournal Of Intelligent & Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGender predictionen_US
dc.subjectstring classificationen_US
dc.subjectsoft clusteringen_US
dc.subjectpath classificationen_US
dc.subjectlevenshteinen_US
dc.subjectfuzzy c-medoidsen_US
dc.subjectFuzzyen_US
dc.subjectBluetoothen_US
dc.subjectBehavioren_US
dc.subjectTrackingen_US
dc.subjectTrajectoriesen_US
dc.subjectRecognitionen_US
dc.subjectSystemen_US
dc.titleFrom indoor paths to gender prediction with soft clusteringen_US
dc.typeArticleen_US

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