关键词: 高维聚类, 混合属性, 客户细分, 图模型

Abstract: In order to divide the telecom customers effectively, a new clustering algorithm for complex attributes was proposed based on feature similarity measurement idea in this paper. In the algorithm, the objects similarities were measured by complex attributes’ distribution similarity function. Then, a graph model was constructed based on the similarity. Finally, the graph was divided to clusters. Compared with the traditional clustering algorithms based on selecting dimension and decreasing dimension, the proposed algorithm can process highdimension data and complex attributes effectively. Meanwhile, it does not need reviewing original date when modifying parameter. Real telecom customer data were used for simulation and the experimental results show that the algorithm can solve customer segmentation problem effectively.

Key words: high-dimension clustering, complex attribute, customer segmentation, graph model