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Data Mining and Knowledge Discovery Handbook, 2 Edition part 64

Data Mining and Knowledge Discovery Handbook, 2 Edition part 64. 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. | 610 Sigal Sahar Bayardo Jr. R. J. Agrawal R. and Gunopulos D. 1999 . Constraint-based rule mining in large dense databases. In Proceedings of the Fifteenth IEEE ICDE International Conference on Data Engineering pages 188-197 Sydney Australia. Brin S. Motwani R. and Silverstein C. 1997 . Beyond market baskets Generalizing association rules to correlations. In Proceedings of ACM SIGMOD International Conference on Management of Data pages 265-276 Tucson AZ USA. Fayyad U. M. Piatetsky-Shapiro G. and Smyth P 1996 . Advances in Knowledge Discovery and Data Mining chapter 1 From Data Mining to Knowledge Discovery An Overview pages 1-34. AAAI Press. Hilderman R. J. and Hamilton H. J. 2000 . Principles for mining summaries using objective measures of interestingness. In Proceedings of the Twelfth IEEE International Conference on Tools with Artificial Intelligence ICTAI pages 72-81 Vancouver Canada. Hilderman R. J. and Hamilton H. J. 2001 . Knowledge Discovery andMeasures of Interest. Kluwer Academic Publishers. Hipp J. and Gunter U. 2002 . Is pushing constraints deeply into the mining algorithms really what we want SIGKDD Explorations 4 1 50-55. Kamber M. and Shinghal R. 1996 . Evaluating the interestingness of characteristic rules. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining pages 263-266 Portland OR USA. Klemettinen M. Mannila H. Ronkainen P. Toivonen H. and Verkam A. I. 1994 . Finding interesting rules from large sets of discovered association rules. In Proceedings of the Third ACM CIKM International Conference on Information and Knowledge Management pages 401-407 Orlando FL USA. ACM Press. Klosgen W. 1996 . Advances in Knowledge Discovery and Data Mining chapter 10 Explora a Multipattern and Multistrategy Discovery Assistant pages 249-271. AAAI Press. Liu B. Hsu W. and Chen S. 1997 . Using general impressions to analyze discovered classification rules. In Proceedings of the Third International Conference on Knowledge .