Annals of Operations Research

, Volume 168, Issue 1, pp 225–245 | Cite as

Chameleon based on clustering feature tree and its application in customer segmentation

  • Jinfeng Li
  • Kanliang Wang
  • Lida Xu


Clustering analysis plays an important role in the filed of data mining. Nowadays, hierarchical clustering technique is becoming one of the most widely used clustering techniques. However, for most algorithms of hierarchical clustering technique, the requirements of high execution efficiency and high accuracy of clustering result cannot be met at the same time. After analyzing the advantages and disadvantages of the hierarchical algorithms, the paper puts forward a two-stage clustering algorithm, named Chameleon Based on Clustering Feature Tree (CBCFT), which hybridizes the Clustering Tree of algorithm BIRCH with algorithm CHAMELEON. By calculating the time complexity of CBCFT, the paper argues that the time complexity of CBCFT increases linearly with the number of data. By experimenting on sample data set, this paper demonstrates that CBCFT is able to identify clusters with large variance in size and shape and is robust to outliers. Moreover, the result of CBCFT is as similar as that of CHAMELEON, but CBCFT overcomes the shortcoming of the low execution efficiency of CHAMELEON. Although the execution time of CBCFT is longer than BIRCH, the clustering result of CBCFT is much satisfactory than that of BIRCH. Finally, through a case of customer segmentation of Chinese Petroleum Corp. HUBEI branch; the paper demonstrates that the clustering result of the case is meaningful and useful.


Data mining Clustering Hierarchical clustering Customer segmentation 


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© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.The School of ManagementXi’an Jiaotong UniversityXi’anChina
  2. 2.Department of Information Technology and Decision ScienceOld Dominion UniversityNorfolkUSA
  3. 3.College of Economics and ManagementBeijing Jiaotong UniversityBeijingChina

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