Insurance Dynamics – A Data Mining Approach for Customer Retention in Health Care Insurance Industry

1. Bently, J. Multidimensional Binary Search Trees Used for Associative searching. - Comm. ACM, Vol. 18, 1975, No 9, 509-517.10.1145/361002.361007Search in Google Scholar

2. Berchtold, S., C. Bohm, H-P. Kriegel. The X-Tree Indexing Structure for High-Dimensional Data. - In: Proc. 22nd Int. Conf. Very Large Database, September 1996, 28-39.Search in Google Scholar

3. Berchtold, S., C. Bohm, H.-P. Kriegel. A Cost Model For Nearest Neighbor Search in High-Dimensional Data Space. - In: Proc. ACM PODS Symp. Principles of Database Systems, 1997.10.1145/263661.263671Search in Google Scholar

4. Berchtold, S., C. Bohm, H.-P. Kriegel. The Pyramid-Technique: Towards Breaking the Course of Dimensionality. - In: Proc. ACM SIGMOD Int. Conference. Management of Data 1998.10.1145/276304.276318Search in Google Scholar

5. Beyer, K., J. Goldstein, R. Ramakri shnan, U. Shaft. When Is "Nearest Neighbor" Meaningful?- In: Proc. Seventh Int. Conf. Database Theory, January1999, 217-235.10.1007/3-540-49257-7_15Search in Google Scholar

6. Breiman, L., J. Friedman, R. Olshen, C. Stone. Classification and Regression Trees. Wadsworth, Pacific Grove, California, 1984.Search in Google Scholar

7. Brown, G. H. Brand Loyalty - Fact or Fiction? - Advertising Age, Vol. 9, 1952, 53-55.Search in Google Scholar

8. Fournier, S., J. L. Yao. Reviving Brand Loyalty: A Reconceptualization Within the Framework of Customer-Brand Relationships. - International Journal of Research in Marketing, Vol. 14, 1997, No 5, 451-472.10.1016/S0167-8116(97)00021-9Search in Google Scholar

9. Francis, L. Neural Networks Demystified. Casualty Actuarial Society Forum, 2001, 252-319.Search in Google Scholar

10. Haberman, S., A. E. Renshaw. Actuarial Applications of Generalized Linear Models. - In: D. J. Hand, S. D. Jacka, Eds. London, Statistics in Finance, Arnold, 1998.Search in Google Scholar

11. Han, J., M. Camber. Data Mining: Concepts and Techniques. New Delhi, India, Morgan Kaufmann Publishers, An Imprint Elsevier, 2001.Search in Google Scholar

12. Hastie, T., R. Tibshirani, J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. - New York, Springer-Verlag, 2001.10.1007/978-0-387-21606-5Search in Google Scholar

13. Jacoby, R. Chesnut. Brand Loyalty: Measurement and Management. New York, Wiley, 1978.Search in Google Scholar

14. Katayama, N., S. Satoh. The SR-Tree: An Index Structure for High-Dimensional Nearest Neighbor Queries. - In: Proc. ACM SIGMOD Int. Conference. Management of Data, May 1997, 517-542.10.1145/253260.253347Search in Google Scholar

15. Kolyshkina, I., D. Steinberg, N. S. Cardell. Using Datamining for Modeling Insurance Risk and Comparison of Datamining and Linear Modeling Approaches. Chapter 14. - In: Intelligent and Computational Techniques in Insurance - Theory and Applications. A. F. Shapiro, L. C. Jain, Eds. Vol. 6. World Scientific Publications, 2003.10.1142/9789812794246_0014Search in Google Scholar

16. Dong-Ho, Lee, Kim Hyoung-Joo. An Efficient Technique for Nearest Neighbour Query Processing on the SPY - TEC. - IEEE Transactions on Knowledge and Data Engineering, Vol. 15, 2003, No 6, 1472-1486.10.1109/TKDE.2003.1245286Search in Google Scholar

17. Lewis, P. A. W., J. Stevens, B. K. Ray. Modeling Time Series Using Multivariate Adaptive Regression Splines (MARS). - In: A. Weigend, N. Gershenfeld, Eds. Time Series Prediction: Forecasting the Future and Understanding the Past, Santa Fe Institute: Assison-Wesley, 1993, 297-318.Search in Google Scholar

18. Lin, K. L., H. V. Jagadish, C. Faloutsos. The TV-Tree: An Index Structure for High-Dimensional Data. - The Very Large Data Bases J., Vol. 3, 1994, No 4, 517-542.10.1007/BF01231606Search in Google Scholar

19. Moore, A. Efficient Memory - Based Learning for Robot Control. Ph. D Thesis, University of Cambridge, 1991.Search in Google Scholar

20. Mowen, J. C. Customer Behaviour. New York, Prentice Hall, 1995.Search in Google Scholar

21. McCullagh, J. A. Nelder. Generalized Linear Models. 2nd Ed. London, Chapman and Hall, 1989.10.1007/978-1-4899-3242-6Search in Google Scholar

22. Salford Systems, Multivariate Adaptive Regression Splines (MARS), 2002. http ://www.salfordsystems.com <http://www.salfordsystems.com>Search in Google Scholar

23. Shapiro, A. F., L. C. Jain. Intelligent and Other Computational Techniques in Insurance - Theory and Applications. - Series on Innovative Intelligence, Vol. 6, World Scientific Publications, Singapore, 2003.10.1142/5441Search in Google Scholar

24. Smyth, G. Generalized Linear Modeling. 2002. http://www.statsci.org/ glm/index.htmlSearch in Google Scholar

25. Steinberg, D., N. S. Cardell. Improving Data Mining with New Hybrid Methods. Boston, MA, DCI Database and Client Server World, 1998.Search in Google Scholar

26. Steinberg, D., N. S. Cardell. The Hybrid CART - Logit Model in Classification and Datamining. - In: Eight Annual Advanced Research Techniques Forum, American Marketing Association, Keystone, Co., 1998.Search in Google Scholar

27. Uncles, M., G. Laurent. Editorial. - International Journal of Research in Marketing, Vol. 14, 1997, No 5, 399-404.10.1016/S0167-8116(97)80224-8Search in Google Scholar

28. Work Cover NSW News, Technology Catches Insurance Fraud, 2001. http ://www.workcover.nsw.gov <http://www.workcover.nsw.gov>. au/pdf/wca46.pdfSearch in Google Scholar