Title :
Mining Customers´ Spatio-Temporal Behavior Data Using Topographic Unsupervised Learning
Author :
Cabanes, Guènaël ; Bennani, Younès ; Dufau-Joel, F.
Author_Institution :
LIPN-CNRS, Villetaneuse, France
Abstract :
Radio frequency identification (RFID) is an advanced tracking technology that can be used to study the spatio-temporal behavior of customers in a supermarket. The aim of this work is to build a new RFID-based autonomous system to follow individuals´ spatio-temporal activity, a tool not currently available, and to develop new methods for automatic data mining. Here, we study how to transform these data to investigate the customers´ behaviors. We propose a new unsupervised data mining method to deal with this complex and very noisy data. This method is fast, efficient and allows some useful analysis to understand how the customers behave during shopping.
Keywords :
consumer behaviour; data mining; radio tracking; radiofrequency identification; retailing; unsupervised learning; RFID-based autonomous system; customer spatio-temporal behavior; radio frequency identification; shopping; spatio-temporal activity; supermarket; topographic unsupervised learning; tracking technology; unsupervised data mining; Data mining; Data structures; Databases; Density functional theory; Machine learning; Prototypes; RFID tags; Radiofrequency identification; Spatiotemporal phenomena; Unsupervised learning; RFID; customers´ behaviors; spatio-temporal activity; unsupervised learning;
Conference_Titel :
Machine Learning and Applications, 2009. ICMLA '09. International Conference on
Conference_Location :
Miami Beach, FL
Print_ISBN :
978-0-7695-3926-3
DOI :
10.1109/ICMLA.2009.23
