Data Mining In The Communications Industry High-end Users Maintain Application

Ministry of Information Industry, the demand for all business development, six of thecommunications industry operators in the fourth reorganization, the new telecommunications and the new China Unicom with the advantages of the balanced development of all business in the new situation to carry out under the fierce competition in the battle for customers, restructured China Mobile and China Railcom's joint short board is very obvious, but the original advantages of mobile services is an absolute can not be underestimated, which after the reorganization of competition in the market to add a lot of variables. The stock of existing customers are the three carriers compete for resources, particularly in high-quality competition for high-end customers is smoke. In this competitive situation, how to prevent the loss of high-end customers, effective to maintain high-end customers are placed in front of several operators a difficult problem, but also the current top priority. Using data mining technology to the loss of high-end early warning customers to ensure that the loss of the former retain customers.The data mining technology is abstracted from huge data of implicit potential, unknown useful information, pattern or trends. The purpose of this paper is to research this kind of technology, and will this technology is applied to the communication enterprises in high-end warning model, to achieve the effective retain in high-end customers. Taking a historical data communications company, as the research object, based on data mining technology, a customer churn prediction model, using the decision tree analysis methods for data mining analysis, more use of telecommunications companies in the mining massive data analysis, the paper the main content as follows:1, describes data mining and data warehousing knowledge of the basic theory and describes the process of data mining as well as several common tools and algorithms.2, the data warehouse design process, know how to build a data warehouse model is a key step in data preparation, including: data extraction, data cleansing, data transformation, data loading and data correction five steps.3, in the communications industry in general, clustering methods can be used for customer segmentation. The customer churn prediction and customer retention issues, and more will use the decision tree method, relatively speaking, decision tree structure and its reasoning process more clearly. Of decision tree, according to algorithm deficiencies in the improved algorithm, using the decision tree algorithm, can achieve greater efficiency while ensuring data quality, analysis of algorithms in the same relatively better.4 introduces the data warehouse design and build, build the prediction model of the process to take six steps: business analysis, data preparation, variable selection, model building, model evaluation and model deployment.5, based on the previous analysis to establish prediction model, the user of a mobile company sample data, the system was verified repeatedly by a large number of data processing and test results have been ideal, and as a basis to develop the corresponding marketing programs on the implementation of the tenure of the work in the high-end users play a very important role.This paper presents a decision tree based on the customer churn warning scheme for the communication enterprises in May in high-end can keep on working and scheme provides certain reference, have certain application value...