Explanation oriented association mining using rough set theory

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

Volumn 2639, Issue , 2003, Pages 165-172

  Yao, Y Y ^a   Zhao, Y ^a   Maguire, R B ^a  

^a UNIVERSITY OF REGINA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; DATA MINING; FUZZY SETS; GRANULAR COMPUTING; PHILOSOPHICAL ASPECTS; DATABASE SYSTEMS; FUZZY CONTROL; LEARNING SYSTEMS; ROUGH SET THEORY; TREES (MATHEMATICS);

ASSOCIATION MINING;

ROUGH SET THEORY; TECHNICAL PRESENTATIONS;

SYNTAX; UPPER APPROXIMATIONS;

EID: 8344259850     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-39205-x_21     Document Type: Conference Paper

References (17)

1. Agrawal, R., Imielinski, T. and Swami, A. Mining association rules between sets of items in large databases, Proceedings of SIGMOD, 207–216. 1993.
2. Berry, M.J.A. and Linoff, G.S. Mastering Data Mining, John Wiley & Sons, Inc., New York, 2000.
3. Bohm, D. and Peat, F.D. Science, Order, and Creativity, 2nd Edition, Rooutledge, London, 2000.
4. Fayyad, U.M., Piatetsky-Shapiro, G. (eds.) Advances in Knowledge Discovery and Data Mining, AAAI Press, 1996.
5. Han, J. and Kamber, M. Data Mining: Concept and Techniques, Morgan Kaufmann Publisher, 2000.
6. Han, J., Lakshmanan, L. and Ng, T. Constraint-based, multidimensional data mining, Computer magzine, 1999.
7. Lin, T.Y., Yao, Y.Y. and Louie, E. Mining value added association rules, Proceedings of PAKDD’02.
8. Ling, C., Chen, T., Yang, Q. and Chen, J. Mining optimal actions for intelligent CRM, Proceedings of ICDM, 2002.
9. Liu, B., Hsu, W. and Ma, Y. Identifying non-actionable association rules, Proceedings of KDD, 329–334, 2001.
10. Pawlak, Z. Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, 1991.
11. Polkowski, L. and Skowron, A. (Eds.) Rough Sets in Knowledge Discovery I, II, Physica-Verlag, Heidelberg, 1998.
12. Rosetta, a rough set toolkit for analyzing data, http://www.idi.ntnu.no/˜aleks/rosetta
13. Wang, K., Zhou, S. and Han, J. Profit mining: from patterns to actions, Proceedings of EDBT, 70–87, 2002.
14. Yao, J.T. and Yao, Y.Y. Induction of classification rules by granular computing, Proceedings of RSCTC’02, 331–338, 2002.
15. Yao, Y.Y. Modeling data mining with granular computing, Proceedings of the 25th Annual International Computer Software and Applications Conference (COMPSAC 2001), 638–643, 2001.
16. Yao, Y.Y. and Zhong, N. An analysis of quantitative measures associated with rules, Proceedings of PAKDD, 479–488, 1999.
17. Zhong, N., Yao, Y.Y., Ohshima, M. and Ohsuga, S. Interestingness, peculiarity, and multi-database mining, Proceedings of ICDM, 566–573, 2001.