In this paper, application of the rough set theory (RST) to feature selection in customer relationship management(CRM) is introduced. Compared to other methods, the RST approach has the advantage of combining bothqualitative and quantitative information in the decision analysis, which is extremely important for CRM. Automateddecision support for CRM has been proposed in recent years. However, little work has been devoted to thedevelopment of computer-based systems to support CRM. This paper presents a novel rough set based algorithm forautomated decision support for CRM. Particularly, the approach is capable to handle real numbers instead of integernumbers through introduction of converted numbers involving tolerances. Being unique and useful in solving CRMproblems, an alternative rule extraction algorithm (AREA) is presented for discovering preference-based rulesaccording to the reducts which contain the maximum of strength index in the same case, where the data withtolerance. The empirical data set associated with CRM has proven the validity and reliability of these approaches.This research thus contributes to developing and validating a useful approach to automated decision support forCRM. This paper forms the basis for solving many other similar problems that occur in the service industry.
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