Doğan, OnurÖztayşi, Başar2022-02-152022-02-1520201064-12461875-8967https://doi.org/10.3233/JIFS-189116https://hdl.handle.net/20.500.14034/203Customer-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.eninfo:eu-repo/semantics/closedAccessGender predictionstring classificationsoft clusteringpath classificationlevenshteinfuzzy c-medoidsFuzzyBluetoothBehaviorTrackingTrajectoriesRecognitionSystemArticle10.3233/JIFS-18911639565296538N/AWOS:0005955206000462-s2.0-85096955754Q2