Measuring attractiveness of rules from the viewpoint of knowledge representation, prediction and efficiency of intervention

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3528 LNAI, Issue , 2005, Pages 11-22

  Slowiński, Roman ^a,b   Greco, Salvatore ^c  

^a POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

^b SYSTEMS RESEARCH INSTITUTE   (Poland)

^c UNIVERSITY OF CATANIA   (Italy)

Author keywords

Attractiveness measures; Dominance based Rough Set Approach; Efficiency of intervention; Knowledge discovery; Rules

Indexed keywords

KNOWLEDGE BASED SYSTEMS; KNOWLEDGE REPRESENTATION; SEMANTICS; STRATEGIC PLANNING;

ATTRACTIVENESS MEASURES; DOMINANCE-BASED ROUGH SET APPROACH; EFFICIENCY OF INTERVENTION; KNOWLEDGE DISCOVERY; RULES;

DATA MINING;

EID: 26944498997     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11495772_3     Document Type: Conference Paper

References (12)

1. Bayardo, R.J., Agrawal, R.: Mining the most interesting rules. [In]: Proc. of the Fifth ACM SIGKDD Intl Conf. on Knowledge Discovery and Data Mining, (1999) 145-154
2. Eells, E., Fitelson, B.: Symmetries and asymmetries in evidential support. Philosophical Studies, 107 (2) (2002) 129-142
3. Fitelson, B.: Studies in Bayesian Confirmation Theory. Ph.D. thesis, University of Wisconsin - Madison (2001)
4. Greco, S., Matarazzo, B., Slowinski, R.: Dominance-Based Rough Set Approach to Knowledge Discovery - (I) General Perspective; (II) Extensions and Applications. Chapters 20 and 21 [in]: N.Zhong and J.Liu, Intelligent Technologies for Information Analysis. Springer-Verlag, New York, (2004) 513-612
5. Greco, S., Matarazzo, B., Pappalardo, N., Słowiński, R.: Measuring expected effects of interventions based on decision rules. Journal of Experimental and Applied Artificial Intelligence, 17 (1-2) (2005) 103-118
6. Greco, S., Pawlak, Z., Słowiński, R.: Can Bayesian confirmation measures be useful for rough set decision rules? Engineering Applications of Artificial Intelligence, 17 (4) (2004) 345-361
7. Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measures of Interest. Kluwer Academic Publishers, Boston (2001)
8. Kyburg, H.: Recent work in inductive logic. [In]: K.G. Lucey and T.R. Machan (eds.), Recent Work in Philosophy. Rowman and Allanheld, Totowa, N.J., (1983) 89-150
9. Popper, K. R.: The Logic of Scientific Discovery. Hutchinson, London (1959)
10. Słowinski, R., Greco, S., Matarazzo, B.: Rough Set Based Decision Support. Chapter 15 [in]: E. Burke and G. Kendall (eds.), Introductory Tutorials on Optimization, Search and Decision Support Methodologies, Springer-Verlag, New York (2005)
11. Tsay, L.-S., Raś, Z.: Action rule discovery: system DEAR2, method and experiments. Journal of Experimental and Applied Artificial Intelligence 17 (1-2) (2005) 119-128
12. Yao, Y.Y., Zhong, N.: An analysis of quantitative measures associated with rules. [In]: Proceedings of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNAI 1574, Springer-Verlag, Berlin, (1999) 479-488