Research On Na(?)ve Bayes Algorithm With Its Application To Customer Churn In Telecommunications

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.