Authors: Mehmed Kantardzic
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 Measures for Categorical Data: A Comparative Evaluation, SIAM
Conference
, 2008, pp. 243–254.
Bow, S.,
Pattern Recognition and Image Preprocessing
, Marcel Dekker, New York, 1992.
Chen, C. H., L. F. Pau, P. S. P. Wang,
Handbook of Pattern Recognition & Computer Vision
, World Scientific Publ. Co., Singapore, 1993.
Dzeroski, S., N. Lavrac, eds.,
Relational Data Mining
, Springer, Berlin, 2001.
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, Elsevier Inc., San Francisco, CA, 2006.
Han, J., et al., Spatial Clustering Methods in Data Mining: A Survey, in
Geographic Data Mining and Knowledge Discovery
, H. Miller, J. Han, eds., Taylor & Francis Publ. Inc., London, 2001.
Hand, D., H. Mannila, P. Smyth,
Principles of Data Mining
, The MIT Press, Cambridge, MA, 2001.
Jain, A. K., Data Clustering: 50 Years Beyond K-Means,
Pattern Recognition Letters
, Vol. 31, No. 8, 2010, pp. 651–666.
Jain, A. K., M. N. Murty, P. J. Flynn, Data Clustering: A Review,
ACM Computing Surveys
, Vol. 31, No. 3, 1999, pp. 264–323.
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.
Karypis, G., E. Han, V. Kumar, Chameleon: Hierarchical Clustering Using Dynamic Modeling,
Computer
, Vol. 32, No. 8, 1999, pp. 68–75.
Lee, I., J. Yang, Common Clustering Algorithms,
Comprehensive Chemometrics
, 2009, Chapter 2.27, pp. 577–618.
Moore, S. K., Understanding the Human Genoma,
Spectrum
, Vol. 37, No. 11, 2000, pp. 33–35.
Munakata, T.,
Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigm
, Springer, New York, 1998.
Norusis, M. J.,
SPSS 7.5: Guide to Data Analysis
, Prentice-Hall, Inc., Upper Saddle River, NJ, 1997.
Poole, D., A. Mackworth, R. Goebel,
Computational Intelligence: A Logical Approach
, Oxford University Press, Inc., New York, 1998.
Tan, P.-N., M. Steinbach, V. Kumar,
Introduction to Data Mining
, Pearson Addison-Wesley, Boston, 2006.
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 with Java Implementations
, Morgan Kaufmannn Publ., Inc., New York, 1999.
CHAPTER 10
Adamo, J.,
Data Mining for Association Rules and Sequential Patterns
, Springer, New York, 2001.
Beyer, K., R. Ramakrishnan, Bottom-Up Computation of Sparse and Iceberg Cubes, Proceedings of 1999 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD’99), Philadelphia, PA, June, 1999, pp. 359–370.
Bollacker, K. D., S. Lawrence, C. L. Giles, Discovering Relevant Scientific Literature on the Web,
IEEE Intelligent Systems
, March/April 2000, pp. 42–47.
Chakrabarti, S., Data Mining for Hypertext: A Tutorial Survey,
SIGKDD Explorations
, Vol. 1, No. 2, 2000, pp. 1–11.
Chakrabarti, S., et al., Mining the Web’s Link Structure,
Computer
, Vol. 32, No. 8, 1999, pp. 60–67.
Chang, G., M. J. Haeley, J. A. M. McHugh, J. T. L. Wang,
Mining the World Wide Web: An Information Search Approach
, Kluwer Academic Publishers, Boston, MA, 2001.
Chen, M., J. Park, P. S. Yu, Efficient Data Mining for Path Traversal Patterns,
IEEE Transactions on Knowledge and Data Engineering
, Vol. 10, No. 2, 1998, pp. 209–214.
Cios, K. J., W. Pedrycz, R. W. Swiniarski, L. A. Kurgan,
Data Mining: A Knowledge Discovery Approach
, Springer, New York, 2007.
Cromp, R. F., W. J. Campbell, Data Mining of Multidimensional Remotely Sansad Images, Proceedings of the CIKM’93 Conference, Washington, DC, 1993, pp. 471–480.
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.
Fayyad, U. M., G. Piatetsky-Shapiro, P. Smith, R. Uthurusamy, eds.,
Advances in Knowledge Discovery and Data Mining
, AAAI Press/MIT Press, Cambridge, 1996.
Fukada, T., Y. Morimoto, S. Morishita, T. Tokuyama, Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization, Proceedings of SIGMOD’96 Conference, Montreal, 1996, pp. 13–23.
Han, J., Towards On-Line Analytical Mining in Large Databases,
SIGMOD Record
, Vol. 27, No. 1, 1998, pp. 97–107.
Han, J., M. Kamber,
Data Mining: Concepts and Techniques
, 2nd edition, Elsevier Inc., San Francisco, CA, 2006.
Han, J., J. Pei, Mining Frequent Patterns by Pattern-Growth: Methodology and Implications,
SIGKDD Explorations
, Vol. 2, No. 2, 2000, pp. 14–20.
Han, E., G. Karypis, V. Kumar, Scalable Parallel Data Mining for Association Rules, Proceedings of the SIGMOD’97 Conference, Tucson, 1997a, pp. 277–288.
Han, J., K. Koperski, N. Stefanovic, GeoMiner: A System Prototype for Spatial Data Mining, Proceedings of the SIGMOD’97 Conference, Arizona, 1997b, pp. 553–556.
Han, J., S. Nishio, H. Kawano, W. Wang, Generalization-Based Data Mining in Object-Oriented Databases Using an Object Cube Model, Proceedings of the CASCON’97 Conference, Toronto, November 1997c, pp. 221–252.
Hedberg, S. R., Data Mining Takes Off at the Speed of the Web,
IEEE Intelligent Systems
, November/December 1999, pp. 35–37.
Hilderman, R. J., H. J. Hamilton,
Knowledge Discovery and Measures of Interest
, Kluwer Academic Publishers, Boston, MA, 2001.
Integral Solutions, 1999, Clementine,
http://www.isl.co.uk/clem.html
.
Kasif, S., Datascope: Mining Biological Sequences,
IEEE Intelligent Systems
, November/December 1999, pp. 38–43.
Kosala, R., H. Blockeel, Web Mining Research: A Survey,
SIGKDD Explorations
, Vol. 2, No. 1, 2000, pp. 1–15.
Kowalski, G. J., M. T. Maybury,
Information Storage and Retrieval Systems: Theory and Implementation
, Kluwer Academic Publishers, Boston, 2000.
Liu, B., W. Hsu, L. Mun, H. Lee, Finding Interesting Patterns Using User Expectations,
IEEE Transactions on Knowledge and Data Engineering
, Vol. 11, No. 6, 1999, pp. 817–825.
McCarthy, J., Phenomenal Data Mining,
CACM
, Vol. 43, No. 8, 2000, pp. 75–79.
Moore, S. K., Understanding the Human Genome,
Spectrum
, Vol. 37, No. 11, 2000, pp. 33–35.
Mulvenna, M. D., et al., eds., Personalization on the Net Using Web Mining, A Collection of Articles,
CACM
, Vol. 43, No. 8, 2000.
Ng, R. T., L. V. S. Lakshmanan, J. Han, A. Pang, Exploratory Mining and Optimization of Constrained Association Queries, Technical Report, University of British Columbia and Concordia University, October 1997.
Park, J. S., M. Chen, P. S. Yu, Efficient Parallel Data Mining for Association Rules, Proceedings of the CIKM’95 Conference, Baltimore, MD, 1995, pp. 31–36.
Pinto, H., J. Han, J. Pei, K. Wang, Q. Chen, U. Dayal, Multi-Dimensional Sequential Pattern Mining, Proc. 2001 Int. Conf. on Information and Knowledge Management (CIKM’01), Atlanta, GA, November 2001.
Salzberg, S. L., Gene Discovery in DNA Sequences,
IEEE Intelligent Systems
, November/December 1999, pp. 44–48.
Spiliopoulou, M., The Laborious Way from Data Mining to Web Log Mining,
Computer Systems in Science & Engineering
, Vol. 2, 1999, pp. 113–125.
Thuraisingham, B.,
Managing and Mining Multimedia Databases
, CRC Press LLC, Boca Raton, FL, 2001.
Witten, I. H., E. Frank,
Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
, Morgan Kaufmannn Publ., Inc., New York, 1999.
Wu, X., et al., Top 10 Algorithms in Data Mining,
Knowledge and Information Systems
, Vol. 14, 2008, pp. 1–37.
Yang, Q., X. Wu, 10 Challenging Problems in Data Mining Research,
International Journal of Information Technology Decision Making
, Vol. 5, No. 4, 2006, pp. 597–604.
CHAPTER 11
Akerkar, R., P. Lingras,
Building an Intelligent Web: Theory and Practice
, Jones and Bartlett Publishers, Sudbury, MA, 2008.
Chang, G., M. J. Haeley, J. A. M. McHugh, J. T. L. Wang,
Mining the World Wide Web: An Information Search Approach
, Kluwer Academic Publishers, Boston, MA, 2001.
Fan, F., L. Wallace, S. Rich, Z. Zhang, Tapping the Power of Text Mining,
Communications of ACM
, Vol. 49, No. 9, 2006, pp. 76–82.