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Data Mining and Knowledge Discovery Handbook, 2 Edition part 94

Data Mining and Knowledge Discovery Handbook, 2 Edition part 94. 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. | 910 Saso DZeroski DZeroski S. Blockeel H. Kompare B. Kramer S. Pfahringer B. and Van Laer W. Experiments in Predicting Biodegradability. In Proceedings of the Ninth International Workshop on Inductive Logic Programming pages 80-91. Springer Berlin 1999. DZeroski S. Relational Data Mining Applications An Overview. In DZeroski and Lavrac 2001 pages 339-364 2001. DZeroski S. De Raedt L. and Wrobel S. editors. Proceedings of the First International Workshop on Multi-Relational Data Mining. KDD-2002 Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Edmonton Canada 2002. Emde W. and Wettschereck D. Relational instance-based learning. In Proceedings of the Thirteenth International Conference on Machine Learning pages 122-130. Morgan Kaufmann San Mateo CA 1996. King R.D. Karwath A. Clare A. and Dehaspe L. Genome scale prediction of protein functional class from sequence using Data Mining. In Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining pages 384-389. ACM Press New York 2000. Kirsten M. Wrobel S. and Horvath T. Distance Based Approaches to Relational Learning and Clustering. In Dzseroski and Lavracs 2001 pages 213-232 2001. Kramer S. Structural regression trees. In Proceedings of the Thirteenth National Conference on Artificial Intelligence pages 812-819. MIT Press Cambridge MA 1996. Kramer S. and Widmer G. Inducing Classification and Regression Trees in First Order Logic. In DZeroski and Lavrac 2001 pages 140-159 2001. Kramer S. Lavrac N. and Flach P. Propositionalization Approaches to Relational Data Mining. In DZseroski and Lavracs 2001 pages 262-291 2001. Lavrac N. DZeroski S. and Grobelnik M. Learning nonrecursive definitions of relations with LINUS. In Proceedings of the Fifth European Working Session on Learning pages 265-281. Springer Berlin 1991. Lavracs N. and DZseroski S. Inductive Logic Programming Techniques and Applications. Ellis Horwood Chichester 1994. Lloyd J. Foundations of Logic