Study On Method Of Customer Classification Based On Rough Set Theory

Data Mining, a new tool and technique for automatic and intelligent database analysis, is a quite active domain for the moment. Presently, Electronic Business is changing traditional business mode all over the world. How can we prevail in the competition of Electronic Business? Customer Relationship Management that can supply customer's resources and involved data analysis is the key, But a huge amount of data prevent us from discovering valuable customer mode, so the research of knowledge discovering system applied in Customer Relationship Management to class reasonably customers is very significative.Now, there are many methods classing customers applied in Customer Relationship Management. But the Rough Set-Based methods are few and they have many advantages. So, this paper is about the research of a Rough Set-Based method classing customers applied in Customer Relationship Management. Firstly, we obtain data from the Customer Relationship Management system, and convert them intorelevant decisiontable. Secondly, we complete and discretize the data in the decisiontable.Thirdly, we reduce the attribute and value. Lastly, we can conclude rules for classing customers and making decision.There may be much uncertain-decision information in Customer Relationship Management, and traditional Rough Set can't be applied to model uncertain-decision information. So in order to solve this problem, we not only use variable precision rough set and ameliorate the arithmetic-reduced used in traditional Rough Set, but also describe this uncertain decision information with weight support and confidence of classing rule.Finally, we carry out the Rough Set-Based method classing customers and obtain comparatively good result of experiment, which prove this paper is available.