【作者】 孙源泽；

【导师】 林亚平；

【作者基本信息】 湖南大学 ， 计算机系统结构， 2008， 硕士

【摘要】 随着国内外电信市场竞争的加剧,客户流失现象成为企业关注的问题之一。面对日益严重的客户流失状况,电信企业需要用数据挖掘技术来分析客户的流失特性,以便采取措施挽留有价值的客户,从而减少客户流失以降低企业的经济损失。因此电信客户流失预测已成为电信行业面临的重要问题。本文重点研究数据挖掘中的朴素贝叶斯分类算法,并将该算法应用到电信行业的客户流失分析中。其主要内容如下:(1)针对属性冗余而导致朴素贝叶斯分类性能降低这一问题,提出了一种改进的选择性朴素贝叶斯算法。该算法先按照属性信息增益值的大小对属性进行排序,然后再对属性进行选择,从而提高了分类的准确率。(2)针对不同级别、不同数量的客户离网后给电信企业带来的离网预测的问题,提出了一种基于最大价值量的朴素贝叶斯算法。该算法通过建立价值量的概念,调整价值敏感属性的价值系数因子,使得离网客户名单中的价值量达到最大。实验仿真结果表明该算法在保持一定的准确率的同时,能预测更多高价值的离网客户。(3)以上述两算法为基础,数据挖掘过程为线索,构建了电信客户流失预测模型。该模型通过改进的选择性朴素贝叶斯算法对属性进行选择,然后利用基于最大价值量的朴素贝叶斯算法进行分类预测,实验仿真结果表明该模型具有较好的分类预测性能。更多还原

【Abstract】 With the rampant competition in the domestic and international wireless telecommunications industry, the customer churning has become one of matters of concern to the enterprise. Faced with the increasingly serious situation in customer churning , telecom enterprises need data mining technology to analyze the churning in order to take measures to maintain valuable customers, and reduce customers churning to lower economic losses. Therefore the prediction of customer churning has become an important issue in telecommunications industry.This theis we focus on the research of Na?ve Bayes classification algorithm, then use the algorithm to analyze the predictation of customer churning in telecommunication. The main contents include:(1)An improved selective Na?ve Bayes algorithm is proposed because correlated features could reduce the performance of the Na?ve Bayes classification. At first the algorithm orders the features by imformation gain, then selects the features in order to improves accuracy.(2)A new churn prediction issue is brought to the telecom company due to different cost taken after different numbers and levels of customers churn, a Na?ve Bayes algorithm based on the maximum value is proposed in this paper .The algorithm can make the value of the churned customer list reach maximization by establishing the concept of value and adjusting the coefficient of the value sensitivity attribute. Experiments result show that the new algorithm can predict more and more valuable churned customers with maintaining certain accuracy.(3)Taking the above two algorithms as the foundation, the process of data mining as the clue, has establish the model of the predication of customer churning. Select the attributes by the improved algorithm of selective Na?ve Bayes, then classify by Na?ve Bayes algorithm based on the maximum value. Experiments result show that the model have a good predicting performance.更多还原