Data Mining by Mehmed Kantardzic (good book recommendations TXT) 📕

Platt, J., Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, in Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Scholkopf, D. Schuurmans, eds., The MIT Press, Cambridge, 1999.

Poole, D., A. Mackworth, R. Goebel, Computational Intelligence: A Logical Approach, Oxford University Press, Inc., New York, 1998.

Pyle, D., Getting the Initial Model: Basic Practices of Data Mining, Business Modeling and Data Mining, 2003, pp. 361–425.

Rao, R., Improved Cardiac Care via Automated Mining of Medical Patient Records, Proceedings of the First International Workshop on Data Mining Case Studies, 2005.

Thrun, S., C. Faloutsos, Automated Learning and Discovery, AI Magazine, Fall 1999, pp. 78–82.

Wu, X., et al., Top 10 Algorithms in Data Mining, Knowledge and Information Systems, Vol. 14, 2008, pp. 1–37.

Xie, Y., An Introduction to Support Vector Machine and Implementation in R, http://yihui.name/cv/images/SVM_Report_Yihui.pdf, May, 2007.

Zhong-Hui, W., W. Li, Y. Cai, X. Xu, An Empirical Comparison of Ensemble Classification Algorithms with Support Vector Machines, Proceedings of the Third International Conference on Machine Laming and Cybernetics, Shanghai, August 2004.

Zweig, M., G. Campbell, Receiver_Operating Characteristic (ROC) Plots: A Fundamental Evaluation Tool in Clinical Medicine, Clinical Chemistry, Vol. 39, No. 4, 1993, pp. 561–576.

CHAPTER 5

Bow, S., Pattern Recognition and Image Preprocessing, Marcel Dekker, New York, 1992.

Brandt, S., Data Analysis: Statistical and Computational Methods for Scientists and Engineers, 3rd edition, Springer, New York, 1999.

Cherkassky, V., F. Mulier, Learning from Data: Concepts, Theory and Methods, John Wiley & Sons, Inc., New York, 1998.

Christensen, R., Log-Linear Models, Springer-Verlag, New York, 1990.

Eddy, W. F., Large Data Sets in Statistical Computing, International Encyclopedia of the Social & Behavioral Sciences, 2004, pp. 8382–8386.

Ezawa, K. J., S. W. Norton, Constructing Bayesian Network to Predict Uncollectible Telecommunications Accounts, IEEE Expert: Intelligent Systems & Their Applications, Vol. 11, No. 5, 1996, pp. 45–51.

Golden, B., E. Condon, S. Lee, E. Wasil, Pre-Processing for Visualization Using Principal Component Analysis, Proceedings of the ANNEC’2000 Conference, St. Louis, 2000, pp. 429–436.

Gose, E., R. Johnsonbaugh, S. Jost, Pattern Recognition and Image Analysis, Prentice Hall, Inc., Upper Saddle River, NJ, 1996.

Han, J., M. Kamber, Data Mining: Concepts and Techniques, 2nd edition, Morgan Kaufmann, San Francisco, CA, 2006.

Hand, D., H. Mannila, P. Smyth, Principles of Data Mining, The MIT Press, Cambridge, MA, 2001.

Jackson, J., Data Mining: A Conceptual Overview, Communications of the Association for Information Systems, Vol. 8, 2002, pp. 267–296.

Jain, A., R. P. W. Duin, J. Mao, Statistical Pattern Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2000, pp. 4–37.

Kennedy, R. L., et al., Solving Data Mining Problems through Pattern Recognition, Prentice Hall, Upper Saddle River, NJ, 1998.

McCullagh, P., J. A. Nelder, Generalized Linear Models, 2nd edition, Chapman & Hall, London, 1994.

Metz, C. E., B. A. Herman, C. A. Roe, Statistical Comparison of Two ROC-Curve Estimates Obtained from Partially-Paired Datasets, Medical Decision Making, Vol. 18, No. 1, 1998, pp. 110–124.

Nisbet, R., J. Elder, G. Miner, Model Evaluation and Enhancement, Handbook of Statistical Analysis and Data Mining Applications, 2009, pp. 285–312.

Norusis, M. J., SPSS 7.5: Guide to Data Analysis, Prentice-Hall, Inc., Upper Saddle River, NJ, 1997.

Smith, M., Neural Networks for Statistical Modeling, Van Nostrand Reinhold Publ., New York, 1993.

Trueblood, R. P., J. N. Lovett, Data Mining and Statistical Analysis Using SQL, Apress, Berkeley, CA, 2001.

Walpore, R. E., R. H. Myers, Probability and Statistics for Engineers and Scientists, 4th edition, Macmillan Publishing Company, New York, 1989.

Witten, I. H., E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmannn Publ., Inc., New York, 1999.

Xie, J., Z. Qiu, The Effect of Imbalanced Data Sets on LDA: A Theoretical and Empirical Analysis, Pattern Recognition, Vol. 40, No. 2, 2007, pp. 557–562.

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

CHAPTER 6

Alpaydin, A., Introduction to Machine Learning, 2nd edition, The MIT Press, Cambridge, 2010.

Cieslak, D. A., N. V. Chawla, Learning Decision Trees for Unbalanced Data, European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Antwerp, Belgium, 2008.

Darlington, J., Y. Guo, J. Sutiwaraphun, H. W. To, Parallel Induction Algorithms for Data Mining, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining KDD’97, 1997, pp. 35–43.

Diettrich, T. G., Machine-Learning Research: Four Current Directions, AI Magazine, Winter 1997, pp. 97–136.

Dzeroski, S., N. Lavrac, eds., Relational Data Mining, Springer, Berlin, 2001.

Finn, P., S. Muggleton, D. Page, A. Srinivasan, Pharmacophore Discovery Using the Inductive Logic Programming System Prolog, Machine Learning, Special Issue on Applications and Knowledge Discovery, Vol. 33, No. 1, 1998, pp. 13–47.

Hand, D., H. Mannila, P. Smyth, Principles of Data Mining, The MIT Press, Cambridge, MA, 2001.

Integral Solutions, 1999, Clementine, http://www.isl.co.uk/clem.html.

John, G. H., Stock Selection Using Rule Induction, IEEE Expert: Intelligent Systems & Their Applications, Vol. 11, No. 5, 1996, pp. 52–58.

King, R. D., et al., Is It Better to Combine Predictions? Protein Engineering, Vol. 13, No. 1, 2000, pp. 15–19.

Leondes, C. T., Knowledge-Based Systems: Techniques and Applications, Academic Press, San Diego, CA, 2000.

Li, W., J. Han, J. Pei, CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules, Proceedings on 2001 International Conference on Data Mining (ICDM’01), San Jose, CA, November 2001.

Luger, G. F., W. A. Stubblefield, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Addison Wesley Longman, Inc., Harlow, England, 1998.

Maimon, O., M. Last, Knowledge Discovery and Data Mining: The Info-Fuzzy Network (IFN) Methodology, Kluwer Academic Publishers, Boston, MA, 2001.

McCarthy, J., Phenomenal Data Mining, CACM, Vol. 43, No. 8, 2000, pp. 75–79.

Mitchell, T. M., Does Machine Learning Really Work? AI Magazine, Fall 1997a, pp. 11–20.

Mitchell, T., Machine Learning, McGraw Hill, New York, 1997b.

Nisbet, R., J. Elder, G. Miner, Classification, 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. 235–258.

Piramuthu, S., Input Data for Decision Trees, Expert Systems with Applications, Vol. 34, No. 2, 2008, pp. 1220–1226.

Poole, D., A. Mackworth, R. Goebel, Computational Intelligence: A Logical Approach, Oxford University Press, Inc., New York, 1998.

Quinlan, J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann