Contingency matrix theory: Statistical dependence in a contingency table

Information Sciences

Volumn 179, Issue 11, 2009, Pages 1615-1627

  Tsumoto, Shusaku ^a  

^a SHIMANE UNIVERSITY   (Japan)

Author keywords

Contingency matrix; Linear algebra; Simpson's paradox; Statistical independence

Indexed keywords

MINING;

CHANCE DISCOVERIES; CONTINGENCY MATRIX; CONTINGENCY TABLES; DATA-MINING; FUNCTIONAL DEPENDENCIES; INTERESTINGNESS; MATRIX THEORIES; SIMPSON'S PARADOX; STATISTICAL DEPENDENCES; STATISTICAL INDEPENDENCE; THEORETICAL RESULTS;

ALGEBRA;

EID: 62249141664     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2008.11.023     Document Type: Article

References (13)

1. In: Coxeter H.S.M. (Ed). Projective Geometry. second ed. (1987), Springer-Verlag, New York
2. Heydtmann M. The nature of truth: Simpson's paradox and the limits of statistical data. Q. J. Med. 95 (2002)
3. Moshkov M., Skowron A., and Suraj Z. Maximal consistent extensions of information systems relative to their theories. Inf. Sci. 178 12 (2008) 2600-2620
4. In: Ohsawa Y., and Tsumoto S. (Eds). Chance Discoveries in Real World Decision Making: Data-based Interaction of Human Intelligence and Artificial Intelligence. Studies in Computational Intelligence vol. 30 (2006), Springer
5. Pawlak Z. Rough sets and intelligent data analysis. Inf. Sci. 147 1-4 (2002) 1-12
6. Pawlak Z., and Skowron A. Rough sets and boolean reasoning. Inf. Sci. 177 1 (2007) 41-73
7. Pawlak Z., and Skowron A. Rough sets: some extensions. Inf. Sci. 177 1 (2007) 28-40
8. Simpson E.H. The interpretation of interaction in contingency tables. J. Roy. Statist. Soc B. 2 (1951)
9. Skowron A., and Grzymala-Busse J. From rough set theory to evidence theory. In: Yager R., Fedrizzi M., and Kacprzyk J. (Eds). Advances in the Dempster-Shafer Theory of Evidence (1994), John Wiley & Sons, New York 193-236
10. Tsumoto S. Automated induction of medical expert system rules from clinical databases based on rough set theory. Inf. Sci. 112 (1998) 67-84
11. Tsumoto S. Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model. Inf. Sci. 162 2 (2004) 65-80
12. Yao Y.Y., and Wong S.K.M. A decision theoretic framework for approximating concepts. IJMMS 37 (1992) 793-809
13. Y.Y. Yao, N. Zhong, An analysis of quantitative measures associated with rules, in: N. Zhong, L. Zhou (Eds.), Methodologies for Knowledge Discovery and Data Mining, Proceedings of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNAI, vol. 1574, Berlin, Springer, 1999, pp. 479-488.