Data Mining and Knowledge Discovery Handbook, 2 Edition part 20. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 170 Lior Rokach and Oded Maimon such as supervised learning lr6 lr12 lr15 unsupervised learning Ir13 lr8 lr5 lr16 and genetic algorithms lr17 lr11 lr1 lr4. References Almuallim H. An Efficient Algorithm for Optimal Pruning of Decision Trees. Artificial Intelligence 83 2 347-362 1996. Almuallim H . and Dietterich . Learning Boolean concepts in the presence of many irrelevant features. Artificial Intelligence 69 1-2 279-306 1994. Alsabti K. Ranka S. and Singh V. CLOUDS A Decision Tree Classifier for Large Datasets Conference on Knowledge Discovery and Data Mining KDD-98 August 1998. Attneave F. Applications of Information Theory to Psychology. Holt Rinehart and Winston 1959. Arbel R. and Rokach L. Classifier evaluation under limited resources Pattern Recognition Letters 27 14 1619-1631 2006 Elsevier. Averbuch M. and Karson T. and Ben-Ami B. and Maimon O. and Rokach L. Contextsensitive medical information retrieval The 11th World Congress on Medical Informatics MEDINFO 2004 San Francisco CA September 2004 IOS Press pp. 282-286. Baker E. and Jain A. K. On feature ordering in practice and some finite sample effects. In Proceedings of the Third International Joint Conference on Pattern Recognition pages 45-49 San Diego CA 1976. BenBassat M. Myopic policies in sequential classification. IEEE Trans. on Computing 27 2 170-174 February 1978. Bennett X. and Mangasarian . Multicategory discrimination via linear programming. Optimization Methods and Software 3 29-39 1994. Bratko I. and Bohanec M. Trading accuracy for simplicity in decision trees Machine Learning 15 223-250 1994. Breiman L. Friedman J. Olshen R. and Stone C. Classification and Regression Trees. Wadsworth Int. Group 1984. Brodley C. E. and Utgoff. P E. Multivariate decision trees. Machine Learning 19 45-77 1995. Buntine W. Niblett T. A Further Comparison of Splitting Rules for Decision-Tree Induction. Machine Learning 8 75-85 1992. Catlett J. Mega induction Machine Learning on Vary Large Databases PhD .
