移动通信经营分析系统的构建与客户流失分析

【作者】 方坤；

【导师】 徐涛；

【作者基本信息】 南京航空航天大学 ， 计算机应用技术， 2004， 硕士

【摘要】 近年来，数据仓库和数据挖掘等新技术的迅速发展为决策支持系统(DSS)的发展开辟了新途径。将决策支持系统由传统的以模型库系统为主体，通过定量分析进行辅助决策转向由数据驱动进行辅助决策，使计算机辅助决策能力上了一个新台阶。目前开发的综合DSS是以数据仓库技术为基础，以联机分析处理和数据挖掘工具为手段进行实施的一整套解决方案。 本文以移动通信经营分析系统为研究背景，根据移动通信行业的数据特点，按照“自底向上”的基本原则，构建面向业务主题的数据集市，并在此基础上最终形成面向整个业务系统的中央数据仓库。在成功构建数据仓库系统之后，针对移动通信行业日益突出的客户流失问题，本文采用了多种理论相互融合的思想，将神经网络和决策树技术相结合，构建客户流失分析模型。文章对神经网络和决策树技术进行了深入的分析，研究其各自的优缺点，并分析了将这两种技术结合在一起的可能性及优势。在客户流失模型的构造过程中，本文针对神经网络算法的缺陷运用了新的改进算法，提高了训练的精度和收敛速度。同时，在传统的决策树算法的分裂准则中成功引入了误分代价的因素，从而提高了分类模型的准确性和适用性。最后通过实际数据对模型进行了应用评估，结果表明这种基于神经网络和决策树技术的预测模型能够对客户流失情况做出准确的预测，达到了商业使用的要求。更多还原

【Abstract】 During the past several years, quick development of Data Warehouse and Data Mining has opened a new approach for Decision Support System (DSS). The transformation from decision support system based on quantitative analysis dominated by modeling system to data-driven system has made a new improvement in computer-aided decision ability. At current, generalized DSS is a set of schemes based on Data Warehouse with the tools of On-Line Analytical Processing and Data Mining.With generalized Business Analysis System of mobile communication as the research background, and according to characters of data in this field, this thesis is based on bottom-up principle to construct business-subject-oriented Data Marts, and ultimately forms a central data warehouse oriented at the whole business system. After successful construction of data warehouse system, this thesis crossly applies several theories to combine technologies of neural network and decision tree. Thus a model of the analysis of customer chum is built to solve the emerging problem of customer churn from mobile communication companies. This thesis also provides an in-depth analysis of neural network and decision tree to find out their respective merits and drawbacks, and performs a research on the superiority of the combination of these two technologies. During the process of constructing the model of analyzing customer churn, an improved algorithm is applied in this thesis directed toward drawbacks of computer network and thus raises the training accuracy and rapidity of convergence. At the same time, after successful absorbent of the factor of misclassification cost into splitting principle of decision tree algorithm, the classification model gets a high improvement in accuracy and adaptation. From the evaluation on models with actual data, it demonstrates that such a predictive model based on neural network and decision tree can provide a comparatively accurate prediction of customer churn and satisfy the requirement of commercial application.更多还原