Abstract
``Customer Retention'' is an increasingly pressing issue intoday's ever-competitive commercial arena. This is especially relevantand important for sales and services related industries. Motivated by areal-world problem faced by a large company, we proposed a solution thatintegrates various techniques of data mining, such as featureselection via induction, deviation analysis, and mining multipleconcept-level association rules to form an intuitive and novel approachto gauging customer loyalty and predicting their likelihood ofdefection. Immediate action triggered by these ``early-warnings''resulting from data mining is often the key to eventual customerretention.
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References
Agrawal, R., Imielinski, T & Swami, A. (1993). DatabaseMining: A Performance Perspective. IEEE Trans. on Knowledge and Data Engineering 5(6): 914-925.
Agrawal, R. & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th VLDB Int'l Conference, Santiago, Chile, 487-499.
Blum, A. & Langley, P. (1997). Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence 97: 245-271.
Clearwater, S., Cheng, T., Hirsh, H. & Buchanan, B. (1989). Incremental Batch Learning. In Segre, A. (ed.) Proceedings of The Sixth International Workshop on Machine Learning, 366-370. Morgan Kaufmann Publishers, Inc.
Dash, M. & Liu, H. (1997). Feature Selection Methods for Classifications. Intelligent Data Analysis: An International Journal 1(3). http://www-east.elsevier.com/ida/free. htm.
Fayyad, U., Piatetsky-Shapiro, G. & Smyth, P. (1996). From Data Mining to Knowledge Discovery: An Overview. In Fayyad, U., Piatetsky-Shapiro, G., Smyth, P. & Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, 495-515. AAAI Press / The MIT Press.
Han, J. & Fu, Y. (1996). Attribute-Oriented Induction in Data Mining. In Fayyad, U., Piatetsky-Shapiro, G., Smyth, P. & Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, 399-421. AAAI Press / The MIT Press.
John, G., Kohavi, R. & Pfleger, K. (1994). Irrelevant Feature and the Subset Selection Problem. In Cohen, W. A. H. H. (ed.) Machine Learning: Proceedings of the Eleventh International Conference, 121-129. New Brunswick, N.J.
Kamber, M., Han, J. & Chiang, J. (1997). Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes. In Proceedings of the 3rd Int'l Conference on Knowledge Discovery and Data Mining (KDD-97), 207.
Levin, R., Rubin, D., Stinson, J. & Gardener, E. (1992). Quantitative Approach to Management (8th Edition), Ch. 3, Forecasting, 103-134. McGraw-Hill.
Liu, H. & Motoda, H. (1998). Feature Selection for Knowledge Discovery Data Mining. Boston: Kluwer Academic Publishers.
Matheus, C., Piatetsky-Shapiro, G. & McNeil, D. (1994). An Application of KEFIR to the Analysis of Healthcare Information. In Proceedings of the 11th Int'l Conference on Artificial Intelligence AAAI-94, Workshop on Knowledge Discovery in Databases, 25-36.
Merz, C. & Murphy, P. (1996). UCI Repository of Machine Learning Databases. http:// www.ics.uci.edu/_mlearn/MLRepository.html. Irvine, CA: University of California, Department of Information and Computer Science.
Piatetsky-Shapiro, G. & Matheus, C. (1994). 'The Interestingness of Deviations. In Proceedings of the 11th Int'l Conference on Artificial Intelligence AAAI-94, Workshop on Knowledge Discovery in Databases, 25-36.
Quinlan, J. (1986). Induction of Decision Trees. Machine Learning 1(1): 81-106.
Quinlan, J. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.
Srikant, R. & Agrawal, R. (1996). Mining Quantitative Association Rules in Large Relational Tables. In Proceedings of ACM SIGMOD Conference on Management of Data. Montreal, Canada.
Widmer, G. (1996). Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning. In Saitta, L. (ed.) Machine Learning: Proceedings of the Thirteenth International Conference, 525-533. Bari, Italy.
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Ng, K., Liu, H. Customer Retention via Data Mining. Artificial Intelligence Review 14, 569–590 (2000). https://doi.org/10.1023/A:1006676015154
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DOI: https://doi.org/10.1023/A:1006676015154