An Agent Model for Incremental Rough Set-Based Rule Induction: A Big Data Analysis in Sales Promotion

Rough set-based rule induction is able to generate decision rules from a database and has mechanisms to handle noise and uncertainty in data. This technique facilitates managerial decision-making and strategy formulation. However, the process for RS-based rule induction is complex and computationally intensive. Moreover, operational databases that are used to run the day-to-day operations, thus large volumes of data are continually updated within a short period of time. The infrastructure required to analyze such large amounts of data must be able to handle extreme data volumes, to allow fast response times, and to automate decisions based on analytical models. This study proposes an Incremental Rough Set-based Rule Induction Agent (IRSRIA). Rule induction is based on creating agents for the main modeling processes. In addition, an incremental architecture is designed, to address large-scale dynamic database problems. A case study of a Home shopping company is used to show the validity and efficiency of this method. The results of experiments show that the IRSRIA can considerably reduce the computation time for inducing decision rules, while maintaining the same quality of rules.