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

Tools and Techniques, 2nd edition, Elsevier Inc., St. Louis, MO, 2005.

CHAPTER 3

Adriaans, P., D. Zantinge, Data Mining, Addison-Wesley Publ. Co., New York, 1996.

Berson, A., S. Smith, K. Thearling, Building Data Mining Applications for CRM, McGraw-Hill, New York, 2000.

Brachman, R. J., T. Khabaza, W. Kloesgen, G. S. Shapiro, E. Simoudis, Mining Business Databases, CACM, Vol. 39, No. 11, 1996, pp. 42–48.

Chen, C. H., L. F. Pau, P. S. P. Wang, Handbook of Pattern Recognition and Computer Vision, World Scientific Publ. Co., Singapore, 1993.

Clark, W. A. V., M. C. Deurloo, Categorical Modeling/Automatic Interaction Detection, Encyclopedia of Social Measurement, 2005, pp. 251–258.

Dwinnell, W., Data Cleansing: An Automated Approach, PC AI, March/April 2001, pp. 21–23.

Eddy, W. F., Large Data Sets in Statistical Computing, in International Encyclopedia of the Social & Behavioral Sciences, N. J. Smelser, P. B. Battes, ed., Pergamon, Oxford, 2004, pp. 8382–8386.

Fayyad, U. M., G. Piatetsky-Shapiro, P. Smith, R. Uthurusamy, eds., Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, Cambridge, 1996.

Groth, R., Data Mining: A Hands-On Approach for Business Professionals, Prentice Hall, Inc., Upper Saddle River, NJ, 1998.

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

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.

Kil, D. H., F. B. Shin, Pattern Recognition and Prediction with Applications to Signal Characterization, AIP Press, Woodburg, NY, 1996.

Liu, H., H. Motoda, eds., Feature Extraction, Construction and Selection: A Data Mining Perspective, Kluwer Academic Publishers, Boston, MA, 1998.

Liu, H., H. Motoda, Feature Selection for Knowledge Discovery and Data Mining, Second Printing, Kluwer Academic Publishers, Boston, 2000.

Liu, H., H. Motoda, eds., Instance Selection and Construction for Data Mining, Kluwer Academic Publishers, Boston, MA, 2001.

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

Pyle, D., Data Preparation for Data Mining, Morgan Kaufmann Publ. Inc., New York, 1999.

Sun, Y., Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 6, 2007, pp. 1035–1051.

Sun, Y., D. Wu, Feature Extraction through Local Learning, Proceedings of the 21st International Conference on Machine Learning, Banff, Canada, 2004.

Sun, Y., D. Wu, A RELIEF Based Feature Extraction Algorithm, Proc. of the 8th SIAM Intl. Conf. Data Mining, 2008.

Tan, P.-N., M. Steinbach, V. Kumar, Introduction to Data Mining, Pearson Addison-Wesley, Boston, 2006.

Wang, Y., F. Makedon, Application of Relief-F Feature Filtering Algorithm to Selecting Informative Genes for Cancer Classification Using Microarray Data, 2004 IEEE Computational Systems Bioinformatics Conference (CSB'04), Stanford, CA, August 2004.

Weiss, S. M., N. Indurkhya, Predictive Data Mining: A Practical Guide, Morgan Kaufman Publishers, Inc., San Francisco, CA, 1998.

Westphal, C., T. Blaxton, Data Mining Solutions: Methods and Tools for Solving Real-World Problems, John Wiley & Sons, Inc., New York, 1998.

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

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.

CHAPTER 4

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

Berbaum, K. S., D. D. Dorfman, E. A. Franken Jr., Measuring Observer Performance by ROC Analysis: Indications and Complications, Investigative Radiology, Vol. 2A, 1989, pp. 228–233.

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

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

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

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

Engel, A., C. Van den Broeck, Statistical Mechanics of Learning, Cambridge University Press, Cambridge, UK, 2001.

Gunopulos, D., R. Khardon, H. Mannila, H. Toivonen, Data Mining, Hypergraph Traversals, and Machine Learning, Proceedings of PODS’97 Conference, Tucson, 1997, pp. 209–216.

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

Hearst, M., Support Vector Machines, IEEE Intelligent Systems, July/August 1998, pp. 18–28.

Hilderman, R. J., H. J. Hamilton, Knowledge Discovery and Measures of Interest, Kluwer Academic Publishers, Boston, MA, 2001.

Hirji, K. K., Exploring Data Mining Implementation, CACM, Vol. 44, No. 7, 2001, pp. 87–93.

Hsu, C., C. Chang, C. Lin, A Practical Guide to Support Vector Classification, http://www.csie.ntu.edu.tw/∼cjlin/papers/guide/guide.pdf, 2009.

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

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

Kitts, B., G. Melli, K. Rexer, eds., Data Mining Case Studies, Proceedings of the First International Workshop on Data Mining Case Studies, 2005.

Kukar, M., Quality Assessment of Individual Classifications in Machine Learning and Data Mining, Knowledge and Information Systems, Vol. 9, No. 3, 2006, pp. 364–384.

Lavrac, N., et al., Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving, Machine Learning, Vol. 57, 2004, pp. 13–34.

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

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

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.

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, 2009a, pp. 235–258.

Nisbet, R., J. Elder, G. Miner, Model Evaluation and Enhancement, in Handbook of Statistical Analysis and Data Mining Applications, R. Nisbet, J. Elder, J. F. Elder, G. Miner, eds., Academic Press, Amsterdam, NL, 2009b, pp. 285–312.

Ortega, P., C. Figueroa, G. Ruz, A Medical Claim Fraud/Abuse Detection System Based on Data Mining: A Case