From indoor paths to gender prediction with soft clustering
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IOS Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Gender prediction, string classification, soft clustering, path classification, levenshtein, fuzzy c-medoids, Fuzzy, Bluetooth, Behavior, Tracking, Trajectories, Recognition, System
