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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 Publ. Inc., San Mateo, CA, 1992.

Russell, S., P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, Upper Saddle River, NJ, 1995.

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

Witten, I. H., E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition, Elsevier Inc., St. Louis, MO, 2005.

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

CHAPTER 7

Benitez, J. M., J. L. Castro, I. Requena, Are Artificial Neural Networks Black Boxes? IEEE Transactions on Neural Networks, Vol. 8, No. 5, 1997, pp. 1156–1164.

Berthold, M., D. J. Hand, eds., Intelligent Data Analysis—An Introduction, Springer, Berlin, 1999.

Castro,