Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Verlag
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Customer 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.
Açıklama
International Conference on Intelligent and Fuzzy Systems, INFUS 2019 -- 23 July 2019 through 25 July 2019 -- -- 228529
Anahtar Kelimeler
Fuzzy C-Medoids, Gender prediction, Indoor paths, Levenshtein distances, Path classification, Decision making, Forecasting, Fuzzy systems, Sales, Competitive environment, Demographic information, Fuzzy classification methods, Gender classification, Gender predictions, Indoor paths, Levenshtein distance, Medoids, Fuzzy set theory
