Mining Customers' Spatio-Temporal Behavior Data Using Topographic Unsupervised Learning

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.