Gender prediction from classified indoor customer paths by fuzzy c-medoids 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.descriptionInternational Conference on Intelligent and Fuzzy Systems, INFUS 2019 -- 23 July 2019 through 25 July 2019 -- -- 228529en_US
dc.description.abstractCustomer oriented systems provides advantages to companies in competitive environment. Understanding customers is a fundamental problem to present individualized offers. Gender information, which is one of the demographic information of customers, mainly cannot be obtained by data collection technologies. Therefore, various techniques are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy set theory. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the study is to classify customer data into the gender classes using indoor paths. © 2020, Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-030-23756-1_21
dc.identifier.endpage169en_US
dc.identifier.isbn9783030237554
dc.identifier.issn2194-5357
dc.identifier.scopus2-s2.0-85069513514en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage160en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-23756-1_21
dc.identifier.urihttps://hdl.handle.net/20.500.14034/201
dc.identifier.volume1029en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.journalAdvances in Intelligent Systems and Computingen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzy C-Medoidsen_US
dc.subjectGender predictionen_US
dc.subjectIndoor pathsen_US
dc.subjectLevenshtein distancesen_US
dc.subjectPath classificationen_US
dc.subjectDecision makingen_US
dc.subjectForecastingen_US
dc.subjectFuzzy systemsen_US
dc.subjectSalesen_US
dc.subjectCompetitive environmenten_US
dc.subjectDemographic informationen_US
dc.subjectFuzzy classification methodsen_US
dc.subjectGender classificationen_US
dc.subjectGender predictionsen_US
dc.subjectIndoor pathsen_US
dc.subjectLevenshtein distanceen_US
dc.subjectMedoidsen_US
dc.subjectFuzzy set theoryen_US
dc.titleGender prediction from classified indoor customer paths by fuzzy c-medoids clusteringen_US
dc.typeConference Objecten_US

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