Read Data Mining Online

Authors: Mehmed Kantardzic

Data Mining (145 page)

Jackson, J., Data Mining: A Conceptual Overview,
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, Vol. 8, 2002, pp. 267–296.

Kennedy, R. L., et al.,
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Platt, J., Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, in
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Thrun, S., C. Faloutsos, Automated Learning and Discovery,
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Wu, X., et al., Top 10 Algorithms in Data Mining,
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CHAPTER 5

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Han, J., M. Kamber,
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Hand, D., H. Mannila, P. Smyth,
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Jackson, J., Data Mining: A Conceptual Overview,
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, Vol. 8, 2002, pp. 267–296.

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, Vol. 22, No. 1, 2000, pp. 4–37.

Kennedy, R. L., et al.,
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, Prentice Hall, Upper Saddle River, NJ, 1998.

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, Vol. 18, No. 1, 1998, pp. 110–124.

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, 2009, pp. 285–312.

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Pattern Recognition
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Yang, Q., X. Wu, Challenging Problems in Data Mining Research,
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, Vol. 5, No. 4, 2006, p. 597.

CHAPTER 6

Alpaydin, A.,
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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,
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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,
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Leondes, C. T.,
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, 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,
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, Addison Wesley Longman, Inc., Harlow, England, 1998.

Maimon, O., M. Last,
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, Kluwer Academic Publishers, Boston, MA, 2001.

McCarthy, J., Phenomenal Data Mining,
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Mitchell, T. M., Does Machine Learning Really Work?
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, Fall 1997a, pp. 11–20.

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, McGraw Hill, New York, 1997b.

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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.

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

Witten, I. H., E. Frank,
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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?
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Berthold, M., D. J. Hand, eds.,
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Cechin, A. L., E. Battistella, The Interpretation of Feedforward Neural Networks for Secondary Structure Prediction Using Sugeno Fuzzy Rules,
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Cherkassky, V., F. Mulier,
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Cios, K. J., W. Pedrycz, R. W. Swiniarski, L. A. Kurgan,
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, Springer, New York, 2007.

Dreyfus, G.,
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Engel, A., C. Van den Broeck,
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Finn, P., S. Muggleton, D. Page, A. Srinivasan, Pharmacophore Discovery Using the Inductive Logic Programming System Prolog,
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, Vol. 33, No. 1, 1998, pp. 13–47.

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

Hagan, M. T., H. B. Demuth, M. Beale,
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Hand, D., H. Mannila, P. Smyth,
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, The MIT Press, Cambridge, MA, 2001.

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

Haykin, S.,
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, 3rd edition, Pearson Education Co., Upper Saddle River, NJ, 2009.

Heaton, J.,
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, 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.

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