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

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, J. L., C. J. Mantas, J. M. Benitez, Interpretation of Artificial Neural Networks by Means of Fuzzy Rules, IEEE Transactions on Neural Networks, Vol. 13, No. 1, 2002, pp. 101–116.

Cechin, A. L., E. Battistella, The Interpretation of Feedforward Neural Networks for Secondary Structure Prediction Using Sugeno Fuzzy Rules, International Journal of Hybrid Intelligent Systems, Vol. 4, No. 1, 2007, pp. 3–16.

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

Cios, K. J., W. Pedrycz, R. W. Swiniarski, L. A. Kurgan, Data Mining: A Knowledge Discovery Approach, Springer, New York, 2007.

Dreyfus, G., Neural Networks: Methodology and Applications, Springer, Berlin, 2005.

Embrechts, M. J., Neural Network for Data Mining, in Intelligent Engineering Systems through Artificial Neural Networks, P. Chen, B. R. Fernandez, J. Gosh, eds., ASME Press, New York, 1995, pp. 771–778.

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

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

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.

Fu, L., Neural Networks in Computer Intelligence, Mc Graw-Hill Inc., New York, 1994.

Fu, L., An Expert Network for DNA Sequence Analysis, IEEE Intelligent Systems, January/February 1999, pp. 65–71.

Hagan, M. T., H. B. Demuth, M. Beale, Neural Network Design, PWS Publishing Co., Boston, 1996.

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

Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, Upper Saddle River, NJ, 1999.

Haykin, S., Neural Networks and Learning Machines, 3rd edition, Pearson Education Co., Upper Saddle River, NJ, 2009.

Heaton, J., Introduction to Neural Networks with Java, Heaton Research, Chesterfield, MD, 2005.

Holena, M., Neural Networks for Extraction of Fuzzy Logic Rules with Application to EEG Data, in Adaptive and Natural Computing Algorithms, B. Ribeiro, ed., Part IV, Springer, Secaucus, NJ, 2005, pp. 369–372.

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

Jang, J. R., C. Sun, Neuro-Fuzzy Modeling and Control, Proceedings of the IEEE, Vol. 83, No. 3, 1995, pp. 378–406.

Jang, J.-S. R., C.-T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Inc., Upper Saddle River, NJ, 1997.

Jin, H., H. Shum, K. Leung, M. Wong, Expanding Self-Organizing Map for Data Visualization and Cluster Analysis, Information Sciences, Vol. 163, Nos. 1–3, 2004, pp. 157–173.

Kanevski, M., Classification of Interest Rate Curves Using Self-Organizing Maps, February 2008, http://arxiv.org/PS_cache/arxiv/pdf/0709/0709.4401v1.pdf.

Kanevski, M., Advanced Mapping of Environmental Data/Geostatistics, Machine Learning and Bayesian Maximum Entropy, EPFL Press, Lausanne, 2008.

Kantardzic, M., A. A. Aly, A. S. Elmaghraby, Visualization of Neural-Network Gaps Based on Error Analysis, IEEE Transactions on Neural Networks, Vol. 10, No. 2, 1999, pp. 419–426.

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

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

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.

Munakata, T., Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigm, Springer, New York, 1998.

Pal, S. K., S. Mitra, Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, John Wiley & Sons, Inc., New York, 1999.

Petlenkov, A., et al., Application of Self-Organizing Kohonen Map to Detection of Surgeon Motions During Endoscopic Surgery, In Proceedings of the 2008 IEEE World Congress on Computational Intelligence (WCCI2008), Hong Kong, 2008.

Rocha, M., P. Cortez, J. Neves, Evolution of Neural Networks for Classification and Regression, Neurocomputing, Vol. 70, No. 16–18, 2007, pp. 2809–2816.

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

Taha, I. A., J. Ghosh, Symbolic Interpretation of Artificial Neural Networks, IEEE Transactions on Knowledge and Data Engineering, Vol. 11, 1999, pp. 448–463.

Van Rooij, A. J. F., L. C. Jain, R. P. Johnson, Neural Network Training Using Genetic Algorithms, World Scientific Publ. Co., Singapore, 1996.

Zurada, J. M., Introduction to Artificial Neural Systems, West Publishing Co., St. Paul, MN, 1992.

CHAPTER 8

Brown, G., Ensemble Learning, in Encyclopedia of Machine Learning, C. Sammut, G. I. Webb, eds., Springer Press, Secaucus, NJ, 2010.

Cios, K. J., W. Pedrycz, R. W. Swiniarski, L. A. Kurgan, Data Mining: A Knowledge Discovery Approach, Springer, New York, 2007.

Dietterich, T. G., Ensemble Methods in Machine Learning, in Lecture Notes in Computer Science on Multiple Classifier Systems, J. Kittler, F. Roli, eds., Vol. 1857, Springer, Berlin/Heidelberg, 2000.

Kuncheva, L. I., Combining Pattern Classifiers: Methods and Algorithms, Wiley, Hoboken, NJ, 2004.

Özyer, T., R. Alhajj, K. Barker, Intrusion Detection by Integrating Boosting Genetic Fuzzy Classifier and Data Mining Criteria for Rule Pre-Screening, Journal of Network and Computer Applications, Vol. 30, No. 1, 2007, pp. 99–113.

Roli, F., Mini Tutorial on Multiple Classifier Systems, School on the Analysis of Patterns, Cagliari, Italy, 2009.

Settles, B., Active Learning Literature Survey, Computer Sciences Technical Report 1648, University of Wisconsin–Madison, January 2010.

Sewell, M., Ensemble Learning, University College London, August 2008. http://machine- learning.martinsewell.com/ensembles/ensemble-learning.pdf.

Stamatatos, E., G. Widmar, Automatic Identification of Music Performers with Learning Ensembles, Artificial Intelligence, Vol. 165, No. 1, 2005, pp. 37–56.

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.

CHAPTER 9

Boriah, S., V. Chandola, V. Kumar, Similarity