[Objective] This paper proposes an adaptive evolutionary clustering framework for contracted customer segmentation with changing cluster structure, aiming to solve the multi-period dynamic customer segmentation problem. [Methods] The proposed framework could track customer segmentation results within a clustering cycle, which updated the proximity matrix and clustering parameters dynamically. For each clustering period, we eliminated expired clusters from the latest adjacent period based on the contract termination date. Then, we calculated the estimated proximity matrix for current customers. We also changed the exiting clusters’ structure according to data of new customers and developed guidelines to add new clusters. Finally, we examined the proposed algorithm with the updated proximity matrix and parameters to obtain the final clustering results of a specific period. [Results] The proposed framework could significantly improve the efficiency of clustering by excluding the process of selecting and matching clusters. [Limitations] The proposed algorithm was not examined with other datasets. [Conclusions] The proposed framework could effectively track evolutionary trajectories of customer groups and eliminate problems facing traditional methods. It could do multi-period dynamic segmentation for contracted customers.
Hiziroglu A.Soft Computing Applications in Customer Segmentation: State-of-art Review and Critique[J]. Expert Systems with Applications, 2013, 40: 6491-6507.
doi: 10.1016/j.eswa.2013.05.052
[3]
Peters G, Weber R.Dynamic Clustering with Soft Computing[J]. WIREs: Data Mining and Knowledge Discovery, 2012, 2(3): 226-236.
doi: 10.1002/widm.1050
[4]
Crespo F, Weber R.A Methodology for Dynamic Data Mining Based on Fuzzy Clustering[J]. Fuzzy Sets and Systems, 2005, 150(2): 267-284.
doi: 10.1016/j.fss.2004.03.028
(Guo Chonghui, Zhao Zuowei.Customer Segmentation and Change Mining Based on Customer Behavior for 4S Shop[J]. Journal of Industrial Engineering Management, 2015, 29(4): 18-26.)
doi: 10.13587/j.cnki.jieem.2015.04.003
[6]
Akhondzadeh-Noughabi E, Albadvi A.Mining the Dominant Patterns of Customer Shifts Between Segments by Using Top-k and Distinguishing Sequential Rules[J]. Management Decision, 2015, 53(9): 1976-2003.
doi: 10.1108/MD-09-2014-0551
[7]
Mark T, Lemon K N, Vandenbosch M, et al.Caputuring the Evolution of Customer-Firm Relationships: How Customers Become More (or Less) Valuable Over Time[J]. Journal of Retailing, 2013, 89(3): 231-245.
doi: 10.1016/j.jretai.2013.04.001
[8]
Benitez I, Quijano A, Diez J L, et al.Dynamic Clustering Segmentation Applied to Load Profiles of Energy Consumption from Spanish Customers[J]. International Journal of Electrical Power & Energy Systems, 2013, 55(2): 437-448.
doi: 10.1016/j.ijepes.2013.09.022
(Ye Qiang, Lu Tao, Yan Xiangbin, et al.Research on Dynamic Customer Segmentation in Customer Relationship Management[J]. Journal of Management Sciences in China, 2006, 9(2): 44-52.)
doi: 10.3321/j.issn:1007-9807.2006.02.006
[10]
Peters G, Weber R, Nowatzke R.Dynamic Rough Clustering and Its Applications[J]. Applied Soft Computing, 2012, 12(10): 3193-3207.
doi: 10.1016/j.asoc.2012.05.015
[11]
Perez-Suarez A, Martínez-Trinidad J F, Carrasco-Ochoa J A, et al. A Dynamic Clustering Algorithm for Building Overlapping Clusters[J]. Intelligent Data Analysis, 2012, 16: 211-232.
doi: 10.3233/IDA-2012-0520
[12]
Perez-Suarez A, Martínez-Trinidad J F, Carrasco-Ochoa J A, et al. An Algorithm Based on Density and Compactness for Dynamic Overlapping Clustering[J]. Pattern Recognition, 2013, 46: 3040-3055.
doi: 10.1016/j.patcog.2013.03.022
[13]
Chi Y, Song X, Zhou D, et al.Evolutionary Spectral Clustering by Incorporating Temporal Smoothness[C]// Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 2007: 153-162.
[14]
Langone R, Suykens J A K. Community Detection Using Kernel Spectral Clustering with Memory[J]. Journal of Physics: Conference Series, 2012, 410: 1-4.
doi: 10.1088/1742-6596/410/1/012100
[15]
Wang Y, Liu S, Feng J, et al.Milling Naturally Smooth Evolution of Clusters from Dynamic Data[C]// Proceedings of the 7th SIAM International Conference on Data Mining. 2007: 125-134.
[16]
Xu T, Zhang Z, Yu P S, et al.Generative Models for Evolutionary Clustering[J]. ACM Transactions on Knowledge Discovery from Data, 2012, 6(2): 1-27.
doi: 10.1145/2297456.2297459
[17]
Li R, Zhang W, Zhao Y, et al.Sparsity Learning Formulations for Mining Time-varying Data[J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(5): 1411-1423.
doi: 10.1109/TKDE.2014.2373411
[18]
Rosswog J, Ghose K.Detecting and Tracking Spatio-temporal Clusters with Adaptive History Filtering[C]// Proceedings of the 2008 IEEE International Conference on Data Mining Workshops, Pisa, Italy. 2008: 448-457.
[19]
Xu K S, Kliger M, Hero III A O. Adaptive Evolutionary Clustering[J]. Data Mining and Knowledge Discovery, 2014, 28(2): 304-336.
doi: 10.1007/s10618-012-0302-x
(Xiao Liyan, Qi Jiayin, Qu Qixing.Study of Transactional Relationship and Customer Risk Based on Customer Behavior[J]. Journal of Beijing University of Posts and Telecommunications: Social Sciences Edition, 2013, 15(2): 86-94.)
[22]
Žalik K R, Žalik B.Validity Index for Clusters of Different Sizes and Densities[J]. Pattern Recognition Letters, 2011, 32(2): 221-234.
doi: 10.1016/j.patrec.2010.08.007
