Affiliations: [a] Department of Industrial Engineering, Izmir Bakircay University, Izmir, Turkey
| [b] Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey
Correspondence:
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Corresponding author .Onur Dogan, Izmir Bakircay University, Department of Industrial Engineering, Izmir,
Turkey. E-mail: onur.dogan@bakircay.edu.tr.
Abstract: Customer-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.
