中文摘要：

针对传统客户价值细分方法在高价值客户细分时不够精细化的问题,引入了大均值子矩阵（LAS）双聚类算法.该方法在客户样本和消费属性两个维度上对消费记录进行双向聚类,可以挖掘出高消费、高价值的客户群体.以某电信公司的高价值客户细分为实例,通过定义一个价值尺度和构建一个PA指标,将所提算法与K均值（K-means）算法进行性能比较,实验结果表明,所提算法能挖掘出更多的高价值客户群体,且能够对客户属性进行更加精细的划分,因此它更适合应用于高价值客户市场的识别和细分.

英文摘要：

To improve the accuracy of traditional method for customer segmentation, the Large Average Submatrix （LAS） biclustering algorithm was used, which performed clusting on customer samples and consumer attributes simultaneously to identify the upscale and high-value customers. By introducing a new value yardstick and a novel index named PA, the LAS biclustering algorithm was compared with K-means clustering algorithm based on a simulation experiment on consumption data of a telecom corporation. The experimental result shows that the LAS biclustering algorithm finds more groups of high-value customers and obtains more accurate clusters. Therefore, it is more suitable for recognition and segmentation of high-value customers.