The algorithm about division and reducts of information system based on discernibility index of attribute

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

Volumn 3309, Issue , 2004, Pages 449-456

  Li, Jun ^a,b,c   Li, Xiongfei ^a,b   Liu, Hui ^a,b   Chen, Xinying ^a,b  

^a JILIN UNIVERSITY   (China)

^b Key Laboratory of Symbolic Computation and Knowledge Engineering in JLU   (China)

^c CHANGCHUN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Data mining; Discernibility index; Reduct; Rough set

Indexed keywords

COMPUTATION THEORY; DATA MINING; INFORMATION SYSTEMS; ROUGH SET THEORY;

DATA MINING AND KNOWLEDGE DISCOVERY; DISCERNIBILITY; REDUCT;

ALGORITHMS;

EID: 35048822606     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30483-8_55     Document Type: Article

References (13)

1. Pawlak Z., Rough sets, International Journal of Computer and Information Sciences,11(1982),341-356.
2. Pawlak Z. et al., Rough Sets, Communication of the ACM,38(1995),189-195.
3. Wong S.K.M, Ziarko W., Optimal Decision Rules in Decision Table, Bulletin of Polish Academy of Sciences, 1985,33(11-12), 693-696.
4. Skowron A, Rauszer C.The Discernibility Matrices and Functions in Information Systems, Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, 1992,331-362.
5. Xiaohua Hu and Cereone N.,Learning in Relational Database: a Rough Sets Approach, Computational Intelligence,1l(1995),323-355.
6. Miao D Q.,Wang J. An Information Based Algorithm for Reduction of Knowledge. Technical Report, Institute of Automation, Chinese Academy of Sciences, March, 1996.
7. Hu Keyun, Research on Concept Lattice and Rough Set Based Data Mining Methods, Ph.D thesis, Tsinghua University, 2001.
8. Bjorvand A T. (1998). 'Rough Enough' - A System Supporting the Rough Sets Approach. http://home.sn.no/~torvill.
9. Kryszkiewicz M, Rybinski H. (1993). Finding Reducts in Composed Information Systems. In: Ziarko W P(Ed.). Proceedings of RSKD'93. London: Springer-Verlag, 261-273.
10. Starzyk J, Nelson D E, Sturtz K., A Mathematical Foundation for Improved Reduct Generation in Information Systems, Knowledge and Information Systems(2000)2, 2000, 131-146.
11. Starzyk J, Nelson D E, Sturtz K. (1999). Reduct Generation in Information System. Bulletin of international rough set society, 3(1/2):19-22.
12. Bazan J., Skowron A, Synak P. Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables. In: Ras Z W, Zemankiva M(Eds.). Methodologies for Intelligent Systems. 1994. Berlin: Springer-Verlag, 346-355.
13. Xiaohua Tony Hu, T.Y.Lin,and Jiaochao Han, A New Rough Sets Model Based on Database Systems, G. Wang et al.(Eds):RSFDGrC 2003, LNAI 2639, 2003, 114-121.