Summary
Systems of formal (symbolic) logic suitable for Data Mining are presented, main stress being put to various kinds of generalized quantifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adamo, J. M. Data Mining for association rules and sequential patterns. Springer 2001.
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and A. I. Verkamo. “Fast discovery of association rules.” In: Advances in knowledge discovery and Data Mining. Fayyad U. M. et al., ed., AAAI Press/MIT Press,1996
Brin, S., Motwani, R., and C. Silverstein. “Beyond market baskets: Generalizing association rules to correlations”. http://citeseer.ist.psu.edu/brin97beyond.html
Chen, G., Wei, Q., and E. E. Kerre. “Fuzzy logic approaches for the mining of association rules: an overview”. In: Data Mining and knowledge discovery approaches based on rule induction techniques (Triantaphyllou E. et al., ed.) Kluwer, 2003
Dehaspe, L., and H. Toivonen. Discovery of frequent Datalog patterns. Data Mining and knowledge discovery 1999; 3:7-36.
Džeroski, S., and N. Lavrač. Relational data mining. Springer, 2001
Ebbinghaus, H. D., Flum, J., and W. Thomas. Mathematical logic. Springer 1984.
Giudici, P. “Data Mining model comparison (Statistical models for Data Mining)”. Chapter 31.4.6, This volume.
Glymour, C., Madigan, D., Pregibon, D., and P. Smyth. “Statistical themes and lessons for Data Mining.” Data Mining and knowledge discovery 1996; 1:25-42.
Hájek, P. “The GUHA method and mining association rules.” Proc. CIMA’2001 (Bangor, Wales) 533-539.
Hájek, P. “The new version of the GUHA procedure ASSOC”, COMPSTAT 1984, 360-365.
Hájek, P. “Generalized quantifiers, finite sets and Data Mining”. In: (Klopotek et al. ed.) Intelligent Information Processing and Web Mining. Springer 2003, 489-496.
Hájek, P., Havel, I. and M. Chytil. “The GUHA method of automatic hypotheses determination”, Computing 1966; 1:293-308.
Hájek, P. and T. Havránek. Mechanizing Hypothesis Formation (Mathematical Foundations for a General Theory), Springer-Verlag 1978, 396 pp.
Hájek, P. and T. Havránek. Mechanizing Hypothesis Formation (Mathematical Foundations for a General Theory). Internet edition (freely accessible) http://www.cs.cas.cz/∼hajek/guhabook/
Hájek, P. and M. Holeňa. “Formal logics of discovery and hypotheses formation by machine”. Theor. Comp. Sci. 2003; 299:245-357.
Hájek, P., Holeňa, M. and J. Rauch. “The GUHA method and foundations of (relational) data mining.” In: (de Swart et al., ed.) Theory and applications of relational structures as knowledge instruments. Lecture Notes in Computer Science vol. 2929, Springer 2003, 17-37.
Hájek, P., Sochorová, A. and J. Zvárová. “GUHA for personal computers”, Comp. Stat. and Data Anal. 1995; 19:149-153.
Hegland, M. “Data Mining techniques”. Acta numerica 2001; 10:313-355.
Holeňa, M. “Exploratory data processing using a fuzzy generalization of the Guha approach”. In: J. F. Baldwin, editor, Fuzzy Logic, John Wiley and Sons, New York 1996, 213-229.
Holeňa, M. “Fuzzy hypotheses for Guha implications”. Fuzzy Sets and Systems, 1998; 98:101–125.
Holeňa, M. “A fuzzy logic framework for testing vague hypotheses with empirical data”. In: Proceedings of the Fourth International ICSC Symposium on Soft Computing and Intelligent Systems for Industry, ICSC Academic Press 2001, 401–407.
Holeňa, M. “A fuzzy logic generalization of a Data Mining approach.” Neural Network World 2001; 11:595–610.
Holeňa, M. “Exploratory data processing using a fuzzy generalization of the GUHA approach”, Fuzzy Logic, Baldwin et al., ed. Willey et Sons New York 1996, 213-229.
Höppner, F. “Association rules”. Chapter 14.7.3, This volume.
Liu,W., Alvarez, S. A., and C. Ruiz. “Collaborative recommendation via adaptive association rule mining”. KDD-2000 Workshop on Web Mining for E-Commerce, Boston, MA.
Rauch, J. “Logical problems of statistical data analysis in databases”. Proc. Eleventh Int. Seminar on Database Management Systems 1988, 53-63.
Rauch, J. “GUHA as a Data Mining Tool, Practical Aspects of Knowledge management”. Schweizer Informatiker Gesellshaft Basel 1996, 10 pp.
Rauch, J. “Logical Calculi for Knowledge Discovery”. Red. Komorowski, J. – Żytkow, J., Berlin, Springer Verlag 1997, 47-57.
Rauch, J.: Classes of Four-Fold Table Quantifiers. In Principles of Data Mining and Knowledge Discovery, (J. Zytkow, M. Quafafou, eds.), Springer-Verlag, 203-211, 1998.
Rauch, J. “Four-fold Table Calculi and Missing Information”. In: JCIS’98 Proceedings, (Paul P. Wang, editor), Association for Intelligent Machinery, 375-378.
Rauch, J., and M. Šimůnek. “Mining for 4ft association rules”. Proc. Discovery Science 2000 Kyoto, Springer Verlag, 268-272.
Rauch, J. and M. Šimůnek. “Mining for statistical association rules”. Proc. PAKDD 2001 Hong Kong, 149-158.
Rauch, J. “Association Rules and Mechanizing Hypothesis Formation”. Working notes of ECML’2001 Workshop: Machine Learning as Experimental Philosophy of Science. See also http://www.informatik.uni-freiburg.de/ ml/ecmlpkdd/.
Rauch, J. and M. Šimůnek. “Mining for 4ft Association Rules by 4ft-Miner”. In: INAP 2001, The Proceeding of the International Rule-Based Data Mining – in conjunction with INAP 2001, Tokyo.
Rauch, J. “Interesting Association Rules and Multi-relational Association Rules”. Communications of Institute of Information and Computing Machinery, Taiwan, 2002; 5, 2:77-82.
Żytkow, J. M. and R. Zembowicz. “Contingency tables as the foundation for concepts, concept hierarchies and rules: the 49er approach”. Fundamenta informaticae 1997; 30:383-399.
GUHA+– project web site http://www.cs.cas.cz/ click Research, Software. http://lispminer.vse.cz/overview/4ftminer.html
Acknowledgments
Partial support of the COST Action 274 (TARSKI) is recognized.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Hájek, P. (2009). Logics for Data Mining. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_26
Download citation
DOI: https://doi.org/10.1007/978-0-387-09823-4_26
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-09822-7
Online ISBN: 978-0-387-09823-4
eBook Packages: Computer ScienceComputer Science (R0)