Scalable classification method based on rough sets

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

Volumn 2475, Issue , 2002, Pages 433-440

  Nguyen, Hung Son ^a  

^a UNIVERSITY OF WARSAW   (Poland)

Author keywords

Data mining; Lazy learning; Rough set; Scalability

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; SCALABILITY;

APRIORI ALGORITHMS; CLASSIFICATION METHODS; CLASSIFICATION TASKS; LAZY LEARNING; ROUGH-SET BASED; ROUGH-SET-BASED METHODS; RULE GENERATION; TIME AND SPACE COMPLEXITY;

ROUGH SET THEORY;

EID: 84942901067     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45813-1_57     Document Type: Conference Paper

References (12)

1. Agrawal R., Mannila H., Srikant R., Toivonen H., Verkamo A.I.: Fast discovery of assocation rules. In V.M. Fayad, G. Piatetsky Shapiro, P. Smyth, R. Uthurusamy (eds): Advanced in Knowledge Discovery and Data Mining, AAAI/MITPress, 1996, pp. 307–328.
2. Aha D.W. (Editorial): “Special Issue on Lazy Learning”, Artificial Intelligence Review, 11(1-5), 1997, pp. 1–6.
3. Bazan J.: A comparison of dynamic non-dynamic rough set methods for extracting laws from decision tables. In: L. Polkowski and A. Skowron (Eds.), Rough Sets in Knowledge Discovery 1, Physica-Verlag, Heidelberg, 1998, pp. 321–365.
4. Komorowski J., Pawlak Z., Polkowski L. and Skowron A.: Rough sets: A tutorial. In: S.K. Pal and A. Skowron (eds.), Rough-fuzzy hybridization: A new trend in decision making, Springer-Verlag, Singapore, 1999, pp. 3–98.
5. Manish Mehta, Rakesh Agrawal, and Jorma Rissanen. SLIQ: A fast scalable classifier for data mining. In Proc. of the Fifth Int’l Conference on Extending Database Technology (EDBT), Avignon, France, March 1996. pp. 18–32
6. Ohrn A., Komorowski J., Skowron A., Synak P.: The ROSETTA Software System. In Polkowski, L., Skowron, A. (Eds.): Rough Sets in Knowledge Discovery Vol. 1,2, Springer Physica-Verlag, Heidelberg, 1998, pp. 572–576.
7. Pawlak Z.: Rough sets: Theoretical aspects of reasoning about data, Kluwer Dordrecht, 1991.
8. J. Shafer, R. Agrawal, and M. Mehta. SPRINT: A scalable parallel classifier for data mining. In Proc. 1996 Int. Conf. Very Large Data Bases, Bombay, India, Sept. 1996, pp. 544–555.
9. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In. R. SOlowiński (ed.). Intelligent Decision Support – Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht, 1992, pp. 311–362
10. Stefanowski J.: On rough set based approaches to induction of decision rules. In: A. Skowron, L. Polkowski (red.), Rough Sets in Knowledge Discovery Vol 1, Physica Verlag, Heidelberg, 1998, 500–529.
11. Wróblewski J., 1998. Covering with reducts – a fast algorithm for rule generation. In L. Polkowski and A. Skowron (Eds.): Proc. of RSCTC’98, Warsaw, Poland. Springer-Verlag, Berlin Heidelberg, pp. 402–407.
12. Ziarko, W.: Rough set as a methodology in Data Mining. In Polkowski, L., Skowron, A. (Eds.): Rough Sets in Knowledge Discovery Vol. 1,2, Springer Physica-Verlag, Heidelberg, 1998, pp. 554–576.