Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Agent-based demand forecast in multi-echelon supply chain
Decision Support Systems
Incremental Bayesian classification for multivariate normal distribution data
Pattern Recognition Letters
Dynamic classification for video stream using support vector machine
Applied Soft Computing
Logic-oriented neural networks for fuzzy neurocomputing
Neurocomputing
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
An agent model for rough classifiers
Applied Soft Computing
Flexible online association rule mining based on multidimensional pattern relations
Information Sciences: an International Journal
Hi-index | 0.00 |
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.