Abstract: With diversification analyses of lost customers in applications, a multi-mode classification algorithm was presented. It applied clustering algorithm to segment the lost customers into many subgroups, then used classification algorithm to build the classifying models on each subgroup. Meanwhile, it filtered the low-precision class models to ensure the precision improvement of forecast lists. Compared with Logistic, decision tree and neural network, the experimental results show that the new classification algorithm has better performance.

Key words: Clustering algorithm, decision tree, neural network, customer retention

摘要： 基于实际应用中的客户流失样本分布多样性的特点，提出了一种基于多模式的分类算法。利用聚类算法对流失客户分析群数据进行划分，得到相应的类群，在此基础上利用分类算法对各划分样本数据类群建立相应的分类模型，同时通过过滤低精确度的分类模型以确保提高分类预测精度。通过与Logistic、决策树、神经网络等方法的实践应用结果表明，新算法在客户流失预测精度上得到了较大的提高。

关键词: 聚类算法, 决策树, 神经网络, 客户保持