Rule induction with grouping target concepts based on rough sets

Electronic Notes in Theoretical Computer Science

Volumn 82, Issue 4, 2003, Pages 286-297

  Tsumoto, Shusaku ^a  

^a SHIMANE UNIVERSITY   (Japan)

Author keywords

Coverage; Granular Computing; Grouping; Rough Sets; Rule induction

Indexed keywords

DATA MINING; DECISION TABLES; EXPERT SYSTEMS; HIERARCHICAL SYSTEMS; LEARNING SYSTEMS; PROBLEM SOLVING; THEOREM PROVING;

COVERAGE; GRANULAR COMPUTING; GROUPING; RULE INDUCTION;

ROUGH SET THEORY;

EID: 18944385836     PISSN: 15710661     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1571-0661(04)80726-6     Document Type: Conference Paper

References (9)

1. Everitt B.S. Cluster Analysis. 1996;3rd Edition, John Wiley & Son, London
2. Pawlak Z. Rough Sets. 1991;Kluwer Academic Publishers, Dordrecht
3. Polkowski L., Skowron A. Rough mereology: a new paradigm for approximate reasoning. Intern. J. Approx. Reasoning. 15:1996;333-365
4. Quinlan J.R. C4.5 - Programs for Machine Learning. 1993;Morgan Kaufmann, Palo Alto
5. Readings in Machine Learning, (Shavlik, J. W. and Dietterich, T.G., eds.) Morgan Kaufmann, Palo Alto, 1990.
6. Skowron A., Grzymala-Busse J. From rough set theory to evidence theory. Yager R., Fedrizzi M., Kacprzyk J. Advances in the Dempster-Shafer Theory of Evidence. 1994;193-236 John Wiley & Sons, New York
7. Tsumoto S. Automated Induction of Medical Expert System Rules from Clinical Databases based on Rough Set Theory. Information Sciences. 112:1998;67-84
8. Tsumoto S. Extraction of Experts' Decision Rules from Clinical Databases using Rough Set Model. Intelligent Data Analysis. 2:(3):1998
9. Zadeh L.A. Toward a theory of fuzzy information granulation and its certainty in human reasoning and fuzzy logic. Fuzzy Sets and Systems. 90:1997;111-127