From indoor paths to gender prediction with soft clustering


Dogan O., Öztayşi B.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, vol.39, no.5, pp.6529-6538, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 39 Issue: 5
  • Publication Date: 2020
  • Doi Number: 10.3233/jifs-189116
  • Title of Journal : JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
  • Page Numbers: pp.6529-6538

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.