doi:

DOI: 10.3724/SP.J.1249.2017.03306

Journal of Shenzhen University Science and Engineering (深圳大学学报理工版) 2017/34:3 PP.306-312

Customer segmentation based on RFM purchase tree


Abstract:
In order to solve the problem that the value of goods has not been considered in traditional methods of customer segmentation, we propose a method of using the recency frequency monetary purchase tree (RFMPT) to represent transaction data, in which a RFM purchase tree is built based on the category of the goods.Based on the RFM purchase tree,we propose a fast clustering algorithm named based recency frequency monetary purchase tree clustering (BRFMPTC). This algorithm constructs the purchase tree as a CoverTree(CT) index structure. With this structure, we can quickly select the k densest purchase trees as cluster centers, then divide the other objects into the nearest class center.The experimental results show that the performance of the proposed method with distance weighting is better than that of the traditional clustering algorithms.

Key words:computer perception,transaction data,customer segmentation,recency frequency monetary purchase tree,cluster,CoverTree,Dunn index

ReleaseDate:2017-06-16 14:08:45



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