Have a personal or library account? Click to login
Insurance Dynamics – A Data Mining Approach for Customer Retention in Health Care Insurance Industry Cover

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

Open Access
|Mar 2013

References

  1. 1. Bently, J. Multidimensional Binary Search Trees Used for Associative searching. - Comm. ACM, Vol. 18, 1975, No 9, 509-517.10.1145/361002.361007
  2. 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.
  3. 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, 1997.10.1145/263661.263671
  4. 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 1998.10.1145/276304.276318
  5. 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_15
  6. 6. Breiman, L., J. Friedman, R. Olshen, C. Stone. Classification and Regression Trees. Wadsworth, Pacific Grove, California, 1984.
  7. 7. Brown, G. H. Brand Loyalty - Fact or Fiction? - Advertising Age, Vol. 9, 1952, 53-55.
  8. 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)00021-9
  9. 9. Francis, L. Neural Networks Demystified. Casualty Actuarial Society Forum, 2001, 252-319.
  10. 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.
  11. 11. Han, J., M. Camber. Data Mining: Concepts and Techniques. New Delhi, India, Morgan Kaufmann Publishers, An Imprint Elsevier, 2001.
  12. 12. Hastie, T., R. Tibshirani, J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. - New York, Springer-Verlag, 2001.10.1007/978-0-387-21606-5
  13. 13. Jacoby, R. Chesnut. Brand Loyalty: Measurement and Management. New York, Wiley, 1978.
  14. 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, 517-542.10.1145/253260.253347
  15. 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_0014
  16. 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.2003.1245286
  17. 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.
  18. 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/BF01231606
  19. 19. Moore, A. Efficient Memory - Based Learning for Robot Control. Ph. D Thesis, University of Cambridge, 1991.
  20. 20. Mowen, J. C. Customer Behaviour. New York, Prentice Hall, 1995.
  21. 21. McCullagh, J. A. Nelder. Generalized Linear Models. 2nd Ed. London, Chapman and Hall, 1989.10.1007/978-1-4899-3242-6
  22. 22. Salford Systems, Multivariate Adaptive Regression Splines (MARS), 2002. http ://www.salfordsystems.com
  23. 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, 2003.10.1142/5441
  24. 24. Smyth, G. Generalized Linear Modeling. 2002. http://www.statsci.org/ glm/index.html
  25. 25. Steinberg, D., N. S. Cardell. Improving Data Mining with New Hybrid Methods. Boston, MA, DCI Database and Client Server World, 1998.
  26. 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.
  27. 27. Uncles, M., G. Laurent. Editorial. - International Journal of Research in Marketing, Vol. 14, 1997, No 5, 399-404.10.1016/S0167-8116(97)80224-8
  28. 28. Work Cover NSW News, Technology Catches Insurance Fraud, 2001. http ://www.workcover.nsw.gov . au/pdf/wca46.pdf
DOI: https://doi.org/10.2478/cait-2012-0004 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 49 - 60
Published on: Mar 13, 2013
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2013 V. Sree Hari Rao, Murthy V. Jonnalagedda, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons License.