Customer Choice of Super Markets using Fuzzy Rough Set on Two Universal Sets and Radial Basis Function Neural Network

Information and communication technology made shopping more convenient for common man. Additionally, customers compare both online and offline price of a commodity. For this reason, offline shopping markets think of customer satisfaction and try to attract customers by various means. But, prediction of customer's choice in an information system is a major issue today. Much research is carried out in this direction for single universe. But, in many real life applications it is observed that relation is established between two universes. To this end, in this paper the authors propose a model to identify customer choice of super markets using fuzzy rough set on two universal sets and radial basis function neural network. The authors use fuzzy rough set on two universal sets on sample data to arrive at customer choice of super markets. The information system with customer choice is further trained with radial basis function neural network for identification of customer choice of supermarkets when customer size increases. A real life problem is presented to show the sustainability of the proposed model.