An agent model for incremental rough set-based rule induction in customer relationship management

Abstract

Compared to other methods, rough set (RS) has the advantage of combining both qualitative and quantitative information in decision analysis, which is extremely important for customer relationship management (CRM). In this paper, we introduce an application of a multi-agent embedded incremental rough set-based rule induction to CRM, namely Incremental Rough Set-based Rule Induction Agent (IRSRIA). The rule induction is based on creating agents within the main modeling processes. This method is suitable for qualitative information and also takes into account user preferences. Furthermore, we designed an incremental architecture for addressing dynamic database problems of rough set-based rule induction, making it unnecessary to re-compute the whole dataset when the database is updated. As a result, huge degrees of computation time and memory space are saved when executing IRSRIA. Finally, we apply our method to a case study of a cell phone purchase. The results show the practical viability and efficiency of this method, and thus this paper forms the basis for solving many other similar problems that occur in the service industry. © 2012 Springer-Verlag.