Support Vector Clustering for Customer Segmentation on Mobile TV Service

References

• Baesens, B., Viaene, S., den Poel, D.V., Vanthienen, J., Dedene, G. (2002). Bayesian neural network learning for repeat purchase modelling in direct marketing. European Journal of Operational Research 138: 191–211.
   Web of Science ®Google Scholar
• Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V. (2002). Support vector clustering. Journal of Machine Learning Research 2: 125–137.
   Web of Science ®Google Scholar
• Berger, P.D., Nasr, N.I. (1998). Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing 12: 17–30.
   Google Scholar
• Breiman, L., Friedman, J., Olshen, R., Stone, C. (1984). Classification and Regression Trees. Monterey, CA: Wadsworth and Brooks.
   Google Scholar
• Cheung, K.-W., Kwok, J.T., Law, M.H., Tsui, K.-C. (2003). Mining customer product ratings for personalized marketing. Decision Support Systems 35: 231–243.
   Web of Science ®Google Scholar
• Chiang, J.-H., Hao, P.-Y. (2003). A new kernel-based fuzzy clustering approach: Support vector clustering with cell growing. IEEE Transactions on Fuzzy Systems 11: 518–527.
   Web of Science ®Google Scholar
• Claeskens, G., Croux, C., Van Kerckhoven, J. (2008). An information criterion for variable selection in support vector machines. Journal of Machine Learning Research 9: 541–558.
   Web of Science ®Google Scholar
• Cristianini, N., Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. 1sted. Cambridge: Cambridge University Press.
   Google Scholar
• Cui, D., Curry, D. (2005). Prediction in marketing using the support vector machine. Marketing Science 24: 595–615.
   Web of Science ®Google Scholar
• Dibb, S. (1998). Market segmentation: strategies for success. Marketing Intelligence & Planning 16: 394–406.
   Google Scholar
• Everitt, B.S., Landau, S., Leese, M. (2009). Cluster Analysis. 4thed. New York: Wiley.
   Google Scholar
• Florez-Lopez, R., Ramon-Jeronimo, J.M. (2008). Marketing segmentation through machine learning models: An approach based on customer relationship management and customer profitability accounting. Social Science Computer Review 27: 96–117.
   Web of Science ®Google Scholar
• Gath, I., Geva, A. (1989). Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 11: 773–780.
   Web of Science ®Google Scholar
• Green, P.E. (1977). A new approach to market segmentation. Business Horizons 20: 61–73.
   Web of Science ®Google Scholar
• Hao, P.-Y., Chiang, J.-H., Tu, Y.-K. (2007). Hierarchically svm classification based on support vector clustering method and its application to document categorization. Expert Systems with Applications 33: 627–635.
   Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R., Friedman, J.H. (2003). The Elements of Statistical Learning. New York: Springer.
   Google Scholar
• Hruschka, H., Natter, M. (1999). Comparing performance of feedforward neural nets and k-means for cluster-based market segmentation. European Journal of Operational Research 114: 346–353.
   Web of Science ®Google Scholar
• Huang, J.-J., Tzeng, G.-H., Ong, C.-S. (2007). Marketing segmentation using support vector clustering. Expert Systems with Applications 32: 313–317.
   Web of Science ®Google Scholar
• Huang, W., Nakamori, Y., Wang, S.-Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research 32: 2513–2522.
   Web of Science ®Google Scholar
• Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems 37: 543–558.
   Web of Science ®Google Scholar
• Jain, A.K., Dubes, R.C. (1988). Algorithms for Clustering Data. Upper Saddle RiverNJ: Prentice-Hall, Inc.
   Google Scholar
• Kaufmann, M. (1990). Readings in Machine Learning (The Morgan Kaufmann Series in Machine Learning). San Mateo, California, USA. Morgan Kaufmann.
   Google Scholar
• Kim, Y., Street, W.N., Russell, G.J., Menczer, F. (2005). Customer targeting: A neural network approach guided by genetic algorithms. Management Science 51: 264–276.
   Web of Science ®Google Scholar
• Lance, G.N., Williams, W.T. (1967). A general theory of classificatory sorting strategies 1. Hierarchical systems. The Computer Journal 9: 373–380.
   Web of Science ®Google Scholar
• Li, S., Davies, B., Edwards, J., Kinman, R., Duan, Y. (2002). Integrating group delphi, fuzzy logic and expert systems for marketing strategy development: The hybridisation and its effectiveness. Marketing Intelligence & Planning 20: 273–284.
   Google Scholar
• Osuna, E., Freund, R., Girosit, F. (1997). Training support vector machines: An application to face detection. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition 6: 130–136.
   Google Scholar
• Peppard, J. (2000). Customer relationship management (crm) in financial services. European Management Journal 18: 312–327.
   Google Scholar
• Rao, V.R., Steckel, J.H. (1995). Selecting, evaluating and updating prospects in direct mail marketing. Journal of Direct Marketing 9(2): 20–31.
   Google Scholar
• Reinartz, W.J., Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing 64: 17–35.
   Web of Science ®Google Scholar
• Schmittlein, D.C., Peterson, R.A. (1994). Customer base analysis: An industrial purchase process application. Marketing Science 13: 41–67.
   Web of Science ®Google Scholar
• Scholkopf, B. (2001). The kernel trick for distances. Advances in Neural Information Processing Systems 13 Proceedings of the 2000 Conference 13: 301–307.
   Google Scholar
• Tax, D. M.J., Duin, R. P.W. (1999). Support vector domain description. Pattern Recognition Letters 20: 1191–1199.
   Web of Science ®Google Scholar
• Venugopal, V., Baets, W. (1994). Neural networks and statistical techniques in marketing research: A conceptual comparison. Marketing Intelligence Planning 12: 30–38.
   Google Scholar
• Wind, Y. (1978). Issues and advances in segmentation research. Journal of Marketing Research 15: 317–337.
   Web of Science ®Google Scholar
• Wolfe, P. (1961). A duality theorem for nonlinear programming. Quarterly of Applied Mathematics 19: 239–244.
   Google Scholar
• Wray, B., Palmer, A., Bejou, D. (1994). Using neural network analysis to evaluate buyer-seller relationships. European Journal of Marketing 28: 32–48.
   Google Scholar
• Xu, B., Zhang, A. (2005). Application of support vector clustering algorithm to network intrusion detection. In: International Conference on Neural Networks and Brain, 2005. ICNN B ’05.Vol. pp. 1036–1040, IEEE.
   Google Scholar
• Yager, R. (2000). Targeted e-commerce marketing using fuzzy intelligent agents. IEEE Intelligent Systems and their Applications 15: 42–45.
   Web of Science ®Google Scholar
• Zhao, Y., Li, B., Li, X., Liu, W., Ren, S. (2005). Customer churn prediction using improved one-class support vector machine. In: Li, X., Wang, S., Dong, Z., eds. Advanced Data Mining and Applications (Lecture Notes in Computer Science). Vol. Berlin/Heidelberg: Springer, pp. 731–731.
   Google Scholar