On efficient construction of decision trees from large databases

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

Volumn 2005, Issue , 2001, Pages 354-361

  Son, Nguyen Hung ^a  

^a UNIVERSITY OF WARSAW   (Poland)

Author keywords

Data mining; Decision tree; Rough set

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DECISION TREES; OPTIMIZATION; QUERY PROCESSING; ROUGH SET THEORY; TREES (MATHEMATICS);

CONTINUOUS ATTRIBUTE; DECISION TREE CONSTRUCTION; EFFICIENT CONSTRUCTION; OPTIMAL PARTITIONS; OPTIMIZATION MEASURES; RELATIONAL DATABASE; SEARCHING SPACES; TIME COMPLEXITY;

DATA MINING;

EID: 84957795281     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45554-X_43     Document Type: Conference Paper

References (14)

1. Chmielewski, M. R., Grzymala-Busse, J. W.: Global discretization of attributes as preprocessing for machine learning. In. T.Y. Lin, A.M. Wildberger (eds.). Soft Computing. Rough Sets, Fuzzy Logic Neural Networks, Uncertainty Management, Knowledge Discovery, Simulation Councils, Inc., San Diego, CA 294–297
2. Dougherty J., Kohavi R., Sahami M.: Supervised and unsupervised discretization of continuous features. In. Proceedings of the Twelfth International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA
3. Fayyad, U. M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Machine Learning 8, 87–102
4. Fayyad, U. M., Irani, K.B.: The attribute selection problem in decision tree generation. In. Proc. of AAAI-92, San Jose, CA. MIT Press
5. J. E. Gehrke, R. Ramakrishnan, and V. Ganti. RAINFOREST - A Framework for Fast Decision Tree Construction of Large Datasets. In Proc. of the 24th International Conference on Very Large Data Bases, New York, New York, 1998.
6. Nguyen, H. Son: Discretization Methods in Data Mining. In L. Polkowski, A. Skowron (Eds.): Rough Sets in Knowledge Discovery 1, Springer Physica-Verlag, Heidelberg, 451–482.
7. H.S. Nguyen and S.H. Nguyen. From Optimal Hyperplanes to Optimal Deciison Trees, Fundamenta Informaticae 34 No 1–2, (1998) 145–174.
8. Nguyen, H. Son: Efficient SQL-Querying Method for Data Mining in Large Data Bases. Proc. of Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99, Morgan Kaufmann Publishers, Stockholm, Sweden, pp. 806-811.
9. Pawlak Z.: Rough sets: Theoretical aspects of reasoning about data, Kluwer Dordrecht.
10. Polkowski, L., Skowron, A. (Eds.): Rough Sets in Knowledge Discovery Vol. 1,2, Springer Physica-Verlag, Heidelberg.
11. Quinlan, J. R. C4.5. Programs for machine learning. Morgan Kaufmann, San Mateo CA.
12. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In. R. SMlowiński (ed.). Intelligent Decision Support – Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht 311–362
13. J. Komorowski, Z. Pawlak, L. Polkowski and A. Skowron,(1998). Rough sets: A tutorial. In: S.K. Pal and A. Skowron (eds.), Rough - fuzzy hybridization: A new trend in decision making, Springer-Verlag, Singapore, pp. 3-98.
14. 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, pp. 554–576.