关键词: 客户细分, 聚类, 集成学习, k均值算法

Abstract: Aiming at the customer segmentation problem of data intensive enterprises,a novel customer segmentation framework based on selective clustering ensemble was developed.In clustering generation step,a unified customer view was constructed based on data sources and business requirements and the customer attributes were split into various subsets,on which a clustering algorithm was run to label the customers with clustering labels.A new evaluation function was introduced to select the most qualified label vectors for clustering ensemble.In clustering ensemble step,an overlap matrix recorded the number of customers that share the same labels was built,and the component clusters for ensemble were selected with the help of overlapping weight.Empirical results on real world customer data demonstrated that the proposed framework could effectively identify customer groups with different values and consumption habits,and thus provided supporting details for business and marketing decision.

Key words: customer segmentation, clustering, ensemble learning, k-means algorithm