基于SOM&SVM组合分类器的客户细分方法实证研究

为解决传统的RFM客户细分方法还不能很好地刻画客户行为,同时也没有就RFM指标权重进行分析这一问题,在RFM指标的基础上扩充了客户细分的指标体系,并提出了基于AHP的RFM指标权重确定策略.鉴于传统的单一分类器存在的很多缺陷,提出基于SOM&SVM的组合分类器模型,充分利用SOM和SVM单一分类器各自的优点,综合两种分类器的分类信息,避免单一分类器可能存在的片面性,从而提高分类的准确性.最后通过实例对上述模型的有效性进行验证.

Empirical Research of Customer Segmentation Methods Based on Som&Svm Combining Classifiers

To solve the problem that the traditional RFM customer segmentation methods can not describe customer behavior preferably and did not analysis the RFM index weight, the customer segmentation index system was expanded on the basis of RFM index and the RFM index weight determination strategy based on AHP was proposed.In view of a great many defects exist in the traditional single classifier,a combining classifiers model based on SOM&SVM was proposed,the respective advantages of SOM&SVM single classifier was fully used,the classified information of two classifiers were synthesized,to avoid one-sidedness may possibly exist in the single classifier,in order to improve the classified accuracy.Finally, effectiveness of the proposed model was Validated by an example.