BOAT - Optimistic Decision Tree Construction

SIGMOD Record (ACM Special Interest Group on Management of Data)

Volumn 28, Issue 2, 1999, Pages 169-180

  Gehrke, Johannes ^a   Ganti, Venkatesh ^a   Ramakrishnan, Raghu ^a   Loh, Wei Yin ^a  

^a University of Wisconsin Madison   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0346457323     PISSN: 01635808     EISSN: None     Source Type: Journal    
DOI: 10.1145/304181.304197     Document Type: Article

References (25)

1. A.C. Davison and D.V. Hinkley. Bootstrap Methods and their Applications. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1997.
2. R. Agrawal, S. Ghosh, T. Imielinski, B. Iyer, and A. Swami. An interval classifier for database mining applications. VLDB 1992.
3. R. Agrawal, T. Imielinski, and A. Swami. Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5(6):914-925, December 1993.
4. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1984.
5. B. Efron and R. J. Tibshirani. An introduction to the bootstrap. Chapman & Hall, 1993.
6. T. Fukuda, Y. Morimoto, and S. Morishita. Constructing efficient decision trees by using optimized numeric association rules. VLDB 1996.
7. T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization. SIGMOD 1996.
8. T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Mining optimized association rules for numeric attributes. PODS 1996.
9. U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
10. J. Gehrke, R. Ramakrishnan, and V. Ganti. Rainforest - A framework for fast decision tree construction of large datasets. VLDB 1996.
11. D.J. Hand. Construction and Assessment of Classification Rules. John Wiley & Sons, Chichester, England, 1997.
12. Tjen-Sien Lim, Wei-Yin Loh, and Yu-Shan Shih. An empirical comparison of decision trees and other classification methods. Technical Report 979, Department of Statistics, University of Wisconsin, Madison, June 1997.
13. Wei-Yin Loh and Yu-Shan Shih. Split selection methods for classification trees. Statistica Sinica, 7(4), October 1997.
14. O. L. Mangasarian. Nonlinear Programming. Classics in Applied Mathematics. Society for Industrial and Applied Mathematics, 1994.
15. M. Mehta, R. Agrawal, and J. 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.
16. Y. Morimoto, T. Fukuda, H. Matsuzawa, T. Tokuyama, and K. Yoda. Algorithms for mining association rules for binary segmentations of huge categorical databases. VLDB 1998.
17. N. Megiddo and R. Srikant. Discovering predictive association rules. KDD 1998.
18. D. Michie, D. J. Spiegelhalter, and C. C. Taylor. Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994.
19. S. K. Murthy. On growing better decision trees from data. PhD thesis, Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, 1995.
20. F. Olken. Random Sampling from Databases. PhD thesis, University of California at Berkeley, 1993.
21. J. Ross Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986.
22. R. Rastogi and K. Shim. Public: A decision tree classifier that integrates building and pruning. VLDB 1998.
23. J. Shafer, R. Agrawal, and M. Mehta. SPRINT: A scalable parallel classifier for data mining. VLDB 1996.
24. P. E. Utgoff, N. C. Berkman, and J. A. Clouse. Decision tree induction based on efficient tree restructuring. Machine Learning, 29:5-44, 1997.
25. P.E. Utgoff. Incremental induction of decision trees. Machine Learning, 4:161-186, 1989.