Doğan, OnurÖztayşi, Başar2022-02-152022-02-15202097830302375542194-5357https://doi.org/10.1007/978-3-030-23756-1_21https://hdl.handle.net/20.500.14034/201International Conference on Intelligent and Fuzzy Systems, INFUS 2019 -- 23 July 2019 through 25 July 2019 -- -- 228529Customer 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.eninfo:eu-repo/semantics/closedAccessFuzzy C-MedoidsGender predictionIndoor pathsLevenshtein distancesPath classificationDecision makingForecastingFuzzy systemsSalesCompetitive environmentDemographic informationFuzzy classification methodsGender classificationGender predictionsIndoor pathsLevenshtein distanceMedoidsFuzzy set theoryConference Object10.1007/978-3-030-23756-1_2110291601692-s2.0-85069513514N/A