The rough set approach to association rule mining

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2003, Pages 529-532

  Guan, J W ^a,b   Bell, D A ^b   Liu, D Y ^a  

^a JILIN UNIVERSITY   (China)

^b QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

Data mining; Knowledge discovery in databases; Rough set

Indexed keywords

EID: 78149308691     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Bell, D.A.; Guan, J. W. 1998, Computational methods for rough classification and discovery, Journal of the American Society for Information Science, Special Topic Issue on Data Mining, Vol.49(1998), No.5, 403-414.
2. Feldman, R.; Aumann, Y.; Amir, A.; Zilberstain, A.; Kloesgen, W. Ben-Yehuda, Y. 1997, Maximal association rules: a new tool for mining for keyword cooccurrences in document collection, in Proceedings of the 3rd International Conference on Knowledge Discovery (KDD 1997), 167-170.
3. Feldman, R.; Fresko, M.; Kinar, Y.; Lindell, Y.; Liphstat, O.; Rajman, M.; Schler, Y.; Zamir, O. 1998, Text mining at the term level, in Proceedings of the 2nd European Symposium on Knowledge Discovery in Databases, PKDD'98, Nantes, France, 23-26 September 1998; Lecture Notes in Artificial Intelligence 1510: Principles of Data Mining and Knowledge Discovery, Jan M. Zytkow Mohamed Quafafou eds.; Springer, 65-73.
4. Guan, J. W. ; Bell, D. A. 1998, Rough computational methods for information systems, Artificial Intelligence - An International Journal, Vol.105(1998), 77-104.
5. Pawlak, Z. (1991). Rough sets: theoretical aspects of reasoning about data. Kluwer.