K-means与朴素贝叶斯在商务智能中的应用

Application Research of K-Means Clustering and Naive Bayesian Algorithm in Business Intelligence

不同的客户给企业带来的效益并不相同,为了提高企业的客户关系管理水平,采用基于K-means的聚类的Naive Bayesian算法来预测客户价值,从而使企业可以针对不同的客户采用不同的营销策略,为企业决策提供依据。朴素贝叶斯分类模型是一种简单有效的分类方法,它理论基础好,分类精度高,由于朴素贝叶斯分类中的独立假设前提,使得在特征选择步骤能否准确有效的分类显得尤为重要。实验结果表明,该算法能在保证一定的准确率的同时,可以预测出更多的潜在高价值客户。

Different customers benefits to enterprise are not the same,in order to improve the level of the enterprise customer relationship management, use the Naive Bayesian algorithm based on the K - means clustering to forecast the customer value ,so that enterprises can use different marketing strategies for different customers. And this will provide a basis for business decisions. Naive Bayesian Classification Model is a simple but efficient solution, and it has solid theory foundation and high accuracy rate of dassifieation, an effective feature selection is very important for an NB- based classifier which uses the conditional independence assumption. Experimental results show that the algorithm can guarantee a certain degree of accuracy and can predict more high- value potential customers.