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Liu, K., H. Kargupta, J. Ryan, Random Projection-Based Multiplicative Data Perturbation for Privacy Preserving Distributed Data Mining, IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 18, No. 1, 2006, pp. 92–106.

Miller, H. J., Geographic Data Mining and Knowledge Discovery, in Handbook of Geographic Information Science”, J. Wilson, A. Stewart Fotheringham, eds., Blackwell Publishing, Malden, MA, 2008.

Nisbet, R., J. Elder, G. Miner, Advanced Algorithms for Data Mining, in Handbook of Statistical Analysis and Data Mining Applications, R. Nisbet, J. Elder, J. F. Elder, G. Miner, eds., Academic Press, Amsterdam, NL, 2009, pp. 151–172.

Pearl, J., Causality, Cambridge University Press, New York, 2000.

Pearl, J., Statistics and Causal Inference: A Review, Sociedad de Estadıstica e Investigaci′n Operativa Test, Vol. 12, No. 2, 2003, pp. 281–345.

Roddick, J. F., M. Spiliopoulou, A Survey of Temporal Knowledge Discovery Paradigms and Methods, IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No. 4, 2002.

Russell, S. J., P. Norvig, Artificial Intelligence, Pearson Education, Upper Saddle River, NJ, 2003.

Shekhar, S., S. Chawla, Introduction to Spatial Data Mining, in Spatial Databases: A Tour, Prentice Hall, Upper Saddle River, NJ, 2003.

Shekhar, S., P. Zhang, Y. Huang, R. Vatsavai, Trends in Spatial Data Mining, in Data Mining: Next Generation Challenges and Future Directions, H. Kargupta, A. Joshi, K. Sivakumar, Y. Yesha, eds., AAAI/MIT Press, Menlo Park, CA, 2004.

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Wu, Q., et al., On Computing Mobile Agent Routes for Data Fusion in Distributed Sensor Networks, IEEE Transactions on Knowledge and Data Engineering, Vol. 16, 2004, pp. 740–753.

Xu, X., N. Yuruk, Z. Feng, T. Schweiger, SCAN: A Structural Clustering Algorithm for Networks, Proceedings of the 13th International Conference on Knowledge Discovery and Data Mining (KDD ’07), New York NY, 2007, pp. 824–833.

Yang, Q., X. Wu, 10 Challenging Problems in Data Mining Research, International Journal of Information Technology and Decision Making, Vol. 5, No. 4, 2006, pp. 597–604.

Yu, H., E.-C. Chang, Distributed Multivariate Regression Based on Influential Observations, The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, August 2003.

Zaki, M., Y. Pan, Introduction: Recent Development in Parallel and Distributed Data Mining, Distributed and Parallel Databases, Vol. 11, No. 2, 2002.

CHAPTER 13

Cox, E., Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration, Morgan Kaufmann, San Francisco, CA, 2005.

Dehuri, S., et al., Genetic Algorithms for Multi-Criterion Classification and Clustering in Data Mining, International Journal of Computing & Information Sciences, Vol. 4, No. 3, 2006, pp. 143–154.

Fogel, D., An Introduction to Simulated Evolutionary Optimization, IEEE Transactions on Neural Networks, Vol. 5, No. 1, 1994, pp. 3–14.

Fogel, D. B., ed., Evolutionary Computation, IEEE Press, New York, 1998.

Fogel, D. B., Evolutionary Computing, Spectrum, Vol. 37, No. 2, 2000, pp. 26–32.

Freitas, A., A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery, in Advances in Evolutionary Computing: Theory and Applications, A. Ghosh, S. Tsutsui, eds., Springer Verlag, New York, 2003.

Goldenberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA, 1989.

Hruschka, E., R. Campello, A. Freitas, A. Carvalho, A Survey of Evolutionary Algorithms for Clustering, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 39, No. 2, 2009, pp. 133–155.

Kaudel, A., M. Last, H. Bunke, eds., Data Mining and Computational Intelligence, Physica-Verlag, Heidelberg, Germany, 2001.

Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin,