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Data Mining and Knowledge Discovery Handbook, 2 Edition part 57

Data Mining and Knowledge Discovery Handbook, 2 Edition part 57. 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. | 540 Yoav Benjamini and Moshe Leshno Storey J.D. Taylor J.E. and Siegmund D. 2004 . Strong control conservative point estimation and simultaneous conservative consistency of false discovery rates A unified approach. Journal of the Royal Statistical Society Series B 66 187-205. Therneau T.M. and Grambsch P.M. 2000 . Modeling Survival Data Extending the Cox Model. Springer. Tibshirani R. and Knight K. 1999 . The covariance inflation criterion for adaptive model selection. Journal of the Royal Statistical Society Series B 61 Part 3 529-546. Zembowicz R. and Zytkov J.M. 1996 . From contingency tables to various froms of knowledge in databases. In U.M. Fayyad R. Uthurusamy G. Piatetsky-Shapiro and P. Smyth editors Advances in Knowledge Discovery and Data Mining pp. 329-349 . MIT Press. Zytkov J.M. and Zembowicz R. 1997 . Contingency tables as the foundation for concepts concept hierarchies and rules The 49er system approach. Fundamenta Informaticae 30 383-399. 26 Logics for Data Mining Petr Hajek Institute of Computer Science Academy of Sciences of the Czech Republic 182 07 Prague Czech Republic hajek@cs.cas.cz Summary. Systems of formal symbolic logic suitable for Data Mining are presented main stress being put to various kinds of generalized quantifiers. Key words logic Data Mining generalized quantifiers GUHA method Introduction Data Mining as presently understood is a broad term including search for association rules classification regression clustering and similar. Here we shall restrict ourselves to search for rules in a rather general sense namely general dependencies valid in given data and expressed by formulas of a formal logical language. The present theoretical approach is the result of a long development of the GUHA method of automated generation of hypotheses General Unary Hypotheses Automaton see a paragraph in Section 26.2 but is believed to be fully relevant for contemporary mining of association rules and its possible generalization. See Agrawal et al.