Applications Of Clustering And Decision Tree Methods In CRM System Of Power Supply Enterprise

The reform of the electricity system allows enterprises to participate in theelectricity market to competition. Under this environment, those companies shouldbuild the new customer relationship management, to impove competition power in themarket.The operating and support system of such enterprises have accumulated a greatdeal of customer consumpting data. Data Mining is an effective tool, taking fulladvantage of valuable data resources, to help us analyze these data correctly and findthe characteristics of customers.Detailed data mining technology including clustering and decision tree methodsare used in this paper, through combining the data warehose and data miningtechnologies and the characteristics of the field of power supply enterprisesknowledge.1. The introduction of Data mining technologyThe correlative basic concepts about data warehouse and data mining areintroducd in the beginning of this paper, such as association rules, decision trees,cluster analysis and others. After all,some possible applications of those methods arediscussed in the power supply enterprise.2. Data preparationBefore applying the data minging algorithems, the original data from differentsources are extracting、transforming and loading to pending request format.3. Modeling1) Customer Segmentation ModelA standard clustering algorithm K-Means is selected for analyzing customer data.For the different customer groups, enterprises can take the different marketingpolicies, to effectively improve the profitability of enterprises. 2) Customer Response ModelAccording to the customer natural information, and spending behaviorinformation, a customer response model is built through one of decision algoriths,SPRINT algorithm.To effectively improve the efficiency of this algorithm, the bestsplit point is optimized in the process of constructing a decision tree, aming at thecharacteristics of continuous and discrete properties. The appropriate threshold is alsoinvestigated for the decision tree generated pre-pruning strategy.