Spectral clustering of customer transaction data with a two-level subspace weighting method

Xiaojun Chen, Wenya Sun, Bo Wang, Zhihui Li, Xizhao Wang, Yunming Ye

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)


Finding customer groups from transaction data is very important for retail and e-commerce companies. Recently, a "Purchase Tree" data structure is proposed to compress the customer transaction data and a local PurTree spectral clustering method is proposed to cluster the customer transaction data. However, in the PurTree distance, the node weights for the children nodes of a parent node are set as equal and the differences between different nodes are not distinguished. In this paper, we propose a two-level subspace weighting spectral clustering (TSW) algorithm for customer transaction data. In the new method, a PurTree subspace metric is proposed to measure the dissimilarity between two customers represented by two purchase trees, in which a set of level weights are introduced to distinguish the importance of different tree levels and a set of sparse node weights are introduced to distinguish the importance of different tree nodes in a purchase tree. TSW learns an adaptive similarity matrix from the local distances in order to better uncover the cluster structure buried in the customer transaction data. Simultaneously, it learns a set of level weights and a set of sparse node weights in the PurTree subspace distance. An iterative optimization algorithm is proposed to optimize the proposed model. We also present an efficient method to compute a regularization parameter in TSW. TSW was compared with six clustering algorithms on ten benchmark data sets and the experimental results show the superiority of the new method.

Original languageEnglish
Pages (from-to)3230-3241
Number of pages12
JournalIEEE Transactions on Cybernetics
Issue number9
Publication statusPublished - Sep 2019
Externally publishedYes


  • Clustering
  • clustering tree
  • customer segmentation
  • two level weighting

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