Data Mining In The Banking Business

The paper presented mainly involves in the designing and implementation a project of bank value-added service. Through the analysis to the value-added service's information, the customers, the products and the competitor, we construct a data warehouse for bank value-added services. We put the emphasis on policy-making support to the analysts and senior management staff, so that they can accurately and timely know the operation state of enterprise, understands the market requirement, and make the correct plan.The paper proposes one recent a new data analysis method to carry on data mining. The method unifies the rough set theory and the multi-dimensional linear regression model in probability statistics, and fully plays their merits after make some improvement to the model. By using the improved method, we analyze the massive records of banking. We not only discover the service rules and carry on twice processing to data with the influence operator, but also obtain the intuitive parametric function and get the analysis information and the policy-making basis.The improved algorithm increases the frequency attribute parameter and establishes the decision-making table with frequency attribute in carries on the data mining in the link. What frequency attribute F record is the object x the number of times which appears in the knowledge library, the value scope is the positive integer, it already does not belong to the condition attribute collection, also does not belong to the policy-making attribute collection. Carries on many step recursion operations to the characteristic collection set, removes small characteristic collection, and withdraws conforms to the condition big characteristic collection[X_k], increases the rule [X_k]â†'Y[Sup][Con] to the regular set in; Meanwhile to conform to the support candidate characteristic collection to carry on combines the production is more Yuan characteristic collection, thus production reflection decision-making by chance information decision rule.The new algorithm carries out the reduction processing to the generated decision rules containing the frequency attribute and obtains the simplest decision rules. Then these new rules are taken as the sample and analyzed through the multivariate linear regression and are executed reprocessing by means of the influence operator. The corresponding multiple linear regression model was established and the local optimum subset was found according to the sample. From them the new multiple linear regression model was built. Finally the least squares method was used to the new regression model to estimate the regression coefficient to carry on the estimate, obtained treats estimates the regression coefficient A_0i=β₀+β₁A₁+β₂A₂+…+β_pA_p=f_i(A₁,…, A_p), i=1, 2,…, p, obtained the direct-viewing parametric function. The user might utilize this group of functions to examine that conveniently the decision-making rests on and obtains intuitively the analysis information.