From indoor paths to gender prediction with soft clustering


Dogan O., Öztayşi B.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, cilt.39, sa.5, ss.6529-6538, 2020 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Konu: 5
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3233/jifs-189116
  • Dergi Adı: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
  • Sayfa Sayıları: ss.6529-6538

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.