Data Clustering with Actuarial Applications

References

• Aggarwal, C. C. 2015. Data mining: The textbook. New York: Springer.
   Google Scholar
• Aggarwal, C. C., and C. K., Reddy, editors. 2013. Data clustering: Algorithms and applications. Boca Raton, FL: CRC Press.
   Google Scholar
• Anderberg, M. 1973. Cluster analysis for applications. New York: Academic Press.
   Google Scholar
• Berry, M., and G. Linoff. 2000. Mastering data mining. New York: John Wiley & Sons.
   Google Scholar
• Bezdek, J. C., R., Ehrlich, and W. Full. 1984. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences 10(2–3): 191–203.
   Web of Science ®Google Scholar
• Bobrowski, L., and J. Bezdek. 1991. c-means clustering with the l1 and l∞ norms. IEEE Transactions on Systems, Man and Cybernetics 21(3): 545–554.
   Web of Science ®Google Scholar
• Bock, H. 1989. Probabilistic aspects in cluster analysis. In Conceptual and numerical analysis of data, 2nd ed. O. Opitz, 12–44, Augsburg: Springer-Verlag.
   Google Scholar
• Bramer, M. 2013. Principles of Data Mining. 2nd ed. New York: Springer.
   Google Scholar
• Campbell, M. 1986. An integrated system for estimating the risk premium of individual car models in motor insurance. ASTIN Bulletin 16(2): 165–183.
   Google Scholar
• Carmichael, J., J., George, and R. Julius. 1968. Finding natural clusters. Systematic Zoology 17(2): 144–150.
   Google Scholar
• Cox, D. R. 1957. Note on grouping. Journal of the American Statistical Association, 52(280): 543–547.
   Web of Science ®Google Scholar
• Dardis, T. 2016. Model efficiency in the U.S. life insurance industry. The Modeling Platform (3): 9–16.
   Google Scholar
• Driver, H. E., and A. L. Kroeber. 1932. Quantitative expression of cultural relationships. University of California Publications in American Archaeology and Ethnology 31(4): 211–256.
   Google Scholar
• Dunn, J. C. 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3(3): 32–57.
   Google Scholar
• Everitt, B. S., S., Landau, M., Leese, and D. Stahl. 2011. Cluster analysis. Hoboken, NJ: Wiley.
   Google Scholar
• Fisher, W. D. 1958. On grouping for maximum homogeneity. Journal of the American Statistical Association 53(284): 789–798.
   Web of Science ®Google Scholar
• Florek, K., J., Lukaszewicz, H., Steinhaus, and S. Zubrzycki. 1951. Sur la liaison et la division des points d’un ensemble fini. Colloquium Mathematicum 2: 282–285.
   Google Scholar
• Frees, E. W. 2009. Regression modeling with actuarial and financial applications. Cambridge: Cambridge University Press.
   Google Scholar
• Gan, G. 2015. A multi-asset Monte Carlo simulation model for the valuation of variable annuities. In Proceedings of the Winter Simulation Conference, 3162–3163. Piscataway, NJ: IEEE Press.
   Google Scholar
• Gan, G. 2018. Valuation of large variable annuity portfolios using linear models with interactions. Risks 6(3): 71.
   Web of Science ®Google Scholar
• Gan, G., and J. Huang. 2017. A data mining framework for valuing large portfolios of variable annuities. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1467–1475.
   Google Scholar
• Gan, G., Q., Lan, and C. Ma. 2016. Scalable clustering by truncated fuzzy c-means. Big Data and Information Analytics 1(2/3): 247–259.
   Google Scholar
• Gan, G., and X. S. Lin. 2017. Efficient Greek calculation of variable annuity portfolios for dynamic hedging: A two-level metamodeling approach. North American Actuarial Journal 21(2): 161–177.
   Web of Science ®Google Scholar
• Gan, G., C., Ma, and J. Wu. 2007. Data clustering: Theory, algorithms, and applications. Philadelphia: SIAM Press.
   Google Scholar
• Gan, G., and E. A. Valdez. 2016. An empirical comparison of some experimental designs for the valuation of large variable annuity portfolios. Dependence Modeling 4(1): 382–400.
   Web of Science ®Google Scholar
• Gan, G., and E. A. Valdez. 2017a. Modeling partial greeks of variable annuities with dependence. Insurance: Mathematics and Econocmics 76: 118–134.
   Web of Science ®Google Scholar
• Gan, G., and E. A. Valdez. 2017b. Valuation of large variable annuity portfolios: Monte carlo simulation and synthetic datasets. Dependence Modeling 5: 354–374.
   Web of Science ®Google Scholar
• Gan, G., and E. A. Valdez. 2018. Regression modeling for the valuation of large variable annuity portfolios. North American Actuarial Journal 22(1): 40–54.
   Web of Science ®Google Scholar
• Gower, J. 1967. A comparison of some methods of cluster analysis. Biometrics 23(4): 623–637.
   PubMed Web of Science ®Google Scholar
• Gower, J. 1971. A general coefficient of similarity and some of its properties. Biometrics 27(4): 857–874.
   Web of Science ®Google Scholar
• Halkidi, M., Y., Batistakis, and M. Vazirgiannis. 2002a. Cluster validity methods: Part I. ACM SIGMOD Record 31(2): 40–45.
   Web of Science ®Google Scholar
• Halkidi, M., Y., Batistakis, and M. Vazirgiannis. 2002b. Clustering validity checking methods: Part II. ACM SIGMOD Record 31(3): 19–27.
   Web of Science ®Google Scholar
• Hardy, M. 2003. Investment guarantees: Modeling and risk management for equity-linked life insurance. Hoboken, NJ: John Wiley and Sons.
   Google Scholar
• Hartigan, J. A. 1975. Clustering algorithms. Probability & Mathematical Statistics. New York: Wiley.
   Google Scholar
• Hejazi, S. A., and K. R. Jackson. 2016. A neural network approach to efficient valuation of large portfolios of variable annuities. Insurance: Mathematics and Economics 70: 169–181.
   Web of Science ®Google Scholar
• Hejazi, S. A., K. R., Jackson, and G. Gan. 2017. A spatial interpolation framework for efficient valuation of large portfolios of variable annuities. Quantitative Finance and Economics 1(2): 125–144.
   Web of Science ®Google Scholar
• Hubert, L., and P. Arabie. 1985. Comparing partitions. Journal of Classification 2: 193–218.
   Web of Science ®Google Scholar
• Institute and Faculty of Actuaries. 2018. What data science means for the future of the actuarial profession: Abstract of the london discussion. British Actuarial Journal 23: e16.
   Google Scholar
• Jain, A., M., Murty, and P. Flynn. 1999. Data clustering: A review. ACM Computing Surveys 31(3): 264–323.
   Web of Science ®Google Scholar
• Jain, A. K., and R. C. Dubes. 1988. Algorithms for clustering data. Upper Saddle River, NJ: Prentice-Hall.
   Google Scholar
• Kassambara, A. 2017. Practical guide to cluster analysis in R: Unsupervised machine learning. CreateSpace Independent Publishing Platform.
   Google Scholar
• Kaufman, L., and P. J. Rousseeuw. 1990. Finding groups in data: An introduction to cluster analysis. Hoboken, NJ: Wiley.
   Google Scholar
• Klawonn, F. 2004. Fuzzy clustering: Insights and a new approach. Mathware & Soft Computing 11: 125–142.
   Google Scholar
• Kogan, J. 2007. Introduction to clustering large and high-dimensional data. Cambridge: Cambridge University Press.
   Google Scholar
• Lance, G., and W. Williams. 1967. A general theory of classificatory sorting strategies I. Hierarchical systems. Computer Journal 9(4): 373–380.
   Web of Science ®Google Scholar
• Lorr, M. 1983. Cluster analysis for social scientists. Jossey-Bass Social and Behavioral Science Series. San Francisco: Jossey-Bass.
   Google Scholar
• Macqueen, J. 1967. Some methods for classification and analysis of multivariate observations. In, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics andProbability, ed. L.LeCamand J.Neyman, vol. 1, 281–297, Berkely: University of California Press.
   Google Scholar
• McQuitty, L. 1957. Elementary linkage analysis for isolating orthogonal and oblique types andtypal relevancies. Educational and Psychological Measurement 17: 207–222.
   Web of Science ®Google Scholar
• Mirkin, B. 1996. Mathematical classification and clustering. New York: Springer.
   Google Scholar
• Nister, D., and H. Stewenius. 2006. Scalable recognition with a vocabulary tree. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, 2161–2168.
   Google Scholar
• O’Hagan, A., and C. Ferrari. 2017. Model-based and nonparametric approaches to clustering for data compression in actuarial applications. North American Actuarial Journal 21(1): 107–146.
   Web of Science ®Google Scholar
• Rohlf, F. 1982. Single link clustering algorithms. In Handbook of statistics, ed. P. Krishnaiah and L. Kanal, vol. 2, 267–284, Amsterdam: North-Holland.
   Google Scholar
• Sebestyen, G. S. 1962. Pattern recognition by an adaptive process of sample set construction. IRE Transactions on Information Theory 8(5): 82–91.
   Google Scholar
• Selim, S., and M. Ismail. 1984. k-means-type algorithms: A generalized convergence theorem and characterizationof local optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence 6(1): 81–87.
   PubMed Web of Science ®Google Scholar
• Sneath, P. 1957. The applications of computers to taxonomy. Journal of General Microbiology 17: 201–226.
   PubMedGoogle Scholar
• Theodoridis, S., and K. Koutroubas. 1999. Pattern recognition. London: Academic Press.
   Google Scholar
• Thorndike, R. L. 1953. Who belongs in the family? Psychometrika 18(4): 267–276.
   Google Scholar
• Tryon, R. C. 1939. Cluster analysis: Correlation profile and orthometric (factor) analysis for the isolation of unities in mind and personality. Ann Arbor, MI: Edwards Brothers.
   Google Scholar
• Ward, J., Jr. 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301): 236–244.
   Web of Science ®Google Scholar
• Ward, J., Jr., and M. Hook. 1963. Application of an hierarchical grouping procedure to a problem of groupingprofiles. Educational and Psychological Measurement 23(1): 69–81.
   Web of Science ®Google Scholar
• Xu, R., and D. Wunsch. 2008. Clustering. Hoboken, NJ: Wiley-IEEE Press.
   Google Scholar
• Xu, W., Y., Chen, C., Coleman, and T. F. Coleman. 2018. Moment matching machine learning methods for risk management of large variable annuity portfolios. Journal of Economic Dynamics and Control 87: 1–20.
   Web of Science ®Google Scholar
• Yao, J. 2016. Clustering in general insurance pricing. International Series on Actuarial Science vol. 2159–179. Cambridge: Cambridge University Press.
   Google Scholar
• Zubin, J. 1938. A technique for measuring like-mindedness. Journal of Abnormal and Social Psychology 33(4): 508–516.
   Google Scholar