Data Mining by Mehmed Kantardzic (good book recommendations TXT) π
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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. 151β172.
Pearl, J., Causality, Cambridge University Press, New York, 2000.
Pearl, J., Statistics and Causal Inference: A Review, Sociedad de EstadΔ±stica e Investigaciβ²n Operativa Test, Vol. 12, No. 2, 2003, pp. 281β345.
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.
Wasserman, S., K. Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, New York, 1994.
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. 740β753.
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 β07), New York NY, 2007, pp. 824β833.
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. 597β604.
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. 143β154.
Fogel, D., An Introduction to Simulated Evolutionary Optimization, IEEE Transactions on Neural Networks, Vol. 5, No. 1, 1994, pp. 3β14.
Fogel, D. B., ed., Evolutionary Computation, IEEE Press, New York, 1998.
Fogel, D. B., Evolutionary Computing, Spectrum, Vol. 37, No. 2, 2000, pp. 26β32.
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. 133β155.
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, 1999.
Munakata, T., Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigm, Springer, New York, 1998.
Navet, N., S. Chen, Financial Data Mining with Genetic Programming: A Survey and Look Forward, The 56th Session of the International Statistical Institute (ISI2007), Lisbon, August 2007.
Salleb-Aouissi, A., C. Christel Vrain, C. Nortet, QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules, Proceedings of the IJCAI-07, 2007, pp. 1035β1040.
Shah, S. C., A. Kusiak, Data Mining and Genetic Algorithm Based Gene/SNP Selection, Artificial Intelligence in Medicine, Vol. 31, No. 3, 2004, pp. 183β196.
Van Rooij, A. J. F., L. C. Jain, R. P. Johnson, Neural Network Training Using Genetic Algorithms, World Scientific Publ. Co., Singapore, 1996.
CHAPTER 14
Chen, S., A Fuzzy Reasoning Approach for Rule-Based Systems Based on Fuzzy Logic, IEEE Transactions on System, Man, and Cybernetics, Vol. 26, No. 5, 1996, pp. 769β778.
Chen, C. H., L. F. Pau, P. S. P. Wang, Handbook of Pattern Recognition & Computer Vision, World Scientific Publ. Co., Singapore, 1993.
Chen, Y., T. Wang, B. Wang, Z. Li, A Survey of Fuzzy Decision Tree Classifier, Fuzzy Information and Engineering, Vol. 1, No. 2, 2009, pp. 149β159.
Cox, E., Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration, Morgan Kaufmann, San Francisco, CA, 2005.
HΓΌllermeier, E., Fuzzy Sets in Machine Learning and Data Mining, Applied Soft Computing, January 2008.
Jang, J. R., C. Sun, Neuro-Fuzzy Modeling and Control, Proceedings of the IEEE, Vol. 83, No. 3, 1995, pp. 378β406.
Jang, J., C. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Inc., Upper Saddle River, NJ, 1997.
Kaudel, A., M. Last, H. Bunke, eds., Data Mining and Computational Intelligence, Physica-Verlag, Heidelberg, Germany, 2001.
Klir, G. J., B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Inc., Upper Saddle River, NJ, 1995.
Koczy, L. T., K. Hirota, Size Reduction by Interpolation in Fuzzy Rule Bases, IEEE Transactions on System, Man, and Cybernetics, Vol. 27, No. 1, 1997, pp. 14β25.
Kruse, R., A. Klose, Recent Advances in Exploratory Data Analysis with Neuro-Fuzzy Methods, Soft Computing, Vol. 8, No. 6, 2004, pp. 381β382.
Laurent, A., M. Lesot, eds., Scalable Fuzzy Algorithms for Data Management and Analysis, Methods and Design, IGI Global, Hershey, PA, 2010.
Lee, E. S., H. Shih, Fuzzy and Multi-level Decision Making: An Interactive Computational Approach, Springer, London, 2001.
Li, H. X., V. C. Yen, Fuzzy Sets and Fuzzy Decision-Making, CRC Press, Inc., Boca Raton, FL, 1995.
Lin, T. Y., N. Cerone, Rough Sets and Data Mining, Kluwer Academic Publishers, Inc., Boston, 1997.
Maimon, O., M. Last, Knowledge Discovery and Data Mining: The Info-Fuzzy Network (IFN) Methodology, Kluwer Academic Publishers, Boston, MA, 2001.
Mendel, J., Fuzzy Logic Systems for Engineering: A Tutorial, Proceedings of the IEEE, Vol. 83, No. 3, 1995, pp. 345β377.
Miyamoto, S., Fuzzy Sets in Information Retrieval and Cluster Analysis, Kluwer Academic Publishers, Dordrecht, 1990.
Munakata, T., Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigm, Springer, New York, 1998.
Γzyer, T., R. Alhajj, K. Barker, Intrusion Detection by Integrating Boosting Genetic Fuzzy Classifier and Data Mining Criteria for Rule
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