Abstract
Recently, a “Purchase Tree” data structure is proposed to compress the customer transaction data and a local PurTree Spectral clustering method is proposed to recover the cluster structure from the purchase trees. However, in the PurTree distance, the node weights for the children nodes of a parent node are set as equal and the difference between different nodes are not distinguished. In this paper, we propose a Structured PurTree Subspace Spectral (SPSS) clustering algorithm for PurTree Data. In the new method, we propose a PurTree subspace similarity to compute the similarity between two trees, in which a set of sparse and structured node weights are introduced to distinguish the importance of different nodes in a purchase tree. A new clustering model is proposed to learn a structured graph with explicit cluster structure. An iterative optimization algorithm is proposed to simultaneously learn the structured graph and node weights. We propose a balanced cover tree for fast k-NN searching during building affinity matrices. SPSS was compared with six clustering algorithms on 10 benchmark data sets and the experimental results show the superiority of the new method.
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References
Beygelzimer, A., Kakade, S., Langford, J.: Cover trees for nearest neighbor. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 97–104. ACM (2006)
Chen, X., Fang, Y., Yang, M., Nie, F., Zhao, Z., Huang, J.Z.: PurTreeClust: a clustering algorithm for customer segmentation from massive customer transaction data. IEEE Trans. Knowl. Data Eng. 30(3), 559–572 (2018). https://doi.org/10.1109/TKDE.2017.2763620
Chen, X., Huang, J.Z., Luo, J.: PurTreeClust: a purchase tree clustering algorithm for large-scale customer transaction data. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 661–672, May 2016. https://doi.org/10.1109/ICDE.2016.7498279
Chen, X., Peng, S., Huang, J.Z., Nie, F., Ming, Y.: Local PurTree spectral clustering for massive customer transaction data. IEEE Intell. Syst. 32(2), 37–44 (2017)
Hagen, L., Kahng, A.B.: New spectral methods for ratio cut partitioning and clustering. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 11(9), 1074–1085 (1992)
Kuo, R., Ho, L., Hu, C.M.: Integration of self-organizing feature map and k-means algorithm for market segmentation. Comput. Oper. Res. 29(11), 1475–1493 (2002)
Lu, T.C., Wu, K.Y.: A transaction pattern analysis system based on neural network. Expert Syst. Appl. 36(3), 6091–6099 (2009)
Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems 2, pp. 849–856 (2002)
Ngai, E.W., Xiu, L., Chau, D.C.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36(2), 2592–2602 (2009)
Nie, F., Wang, X., Huang, H.: Clustering and projected clustering with adaptive neighbors. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 977–986. ACM (2014)
Nie, F., Wang, X., Jordan, M.I., Huang, H.: The constrained Laplacian rank algorithm for graph-based clustering. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 1969–1976 (2016)
Tsai, C.Y., Chiu, C.C.: A purchase-based market segmentation methodology. Expert Syst. Appl. 27(2), 265–276 (2004)
Wang, K., Xu, C., Liu, B.: Clustering transactions using large items. In: Proceedings of the Eighth International Conference on Information and Knowledge Management, pp. 483–490. ACM (1999)
Wang, W., Carreira-Perpián, M.Á.: Projection onto the probability simplex: an efficient algorithm with a simple proof, and an application. Mathematics (2013)
Xiao, Y., Dunham, M.H.: Interactive clustering for transaction data. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 121–130. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44801-2_13
Xiong, T., Wang, S., Mayers, A., Monga, E.: DHCC: Divisive hierarchical clustering of categorical data. Data Mining Knowl. Discovery 24(1), 103–135 (2012)
Acknowledgment
This research was supported by the National Key R&D Program of China 2018YFB1003201, NSFC under Grant no. 61773268, 61502177 and U1636202, and Guangdong Key Laboratory project 2017B030314073.
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Chen, X., Guo, C., Fang, Y., Mao, R. (2019). Structured Spectral Clustering of PurTree Data. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_29
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DOI: https://doi.org/10.1007/978-3-030-18579-4_29
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