Algorithms for finding attribute value group for binary segmentation of categorical databases

IEEE Transactions on Knowledge and Data Engineering

Volumn 14, Issue 6, 2002, Pages 1269-1279

  Morimoto, Yasuhiko ^a   Fukuda, Takeshi ^a   Tokuyama, Takeshi ^b  

^a IBM RESEARCH TOKYO   (Japan)

^b TOHOKU UNIVERSITY   (Japan)

Author keywords

Binary segmentation; Categorical test; Data mining; Data reduction; Decision tree; Value groups

Indexed keywords

ALGORITHMS; COMPUTATIONAL GEOMETRY; DATA MINING; DATA REDUCTION; DATABASE SYSTEMS; HEURISTIC METHODS; PROBLEM SOLVING;

BINARY SEGMENTATION;

KNOWLEDGE ENGINEERING;

EID: 0036859049     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2002.1047767     Document Type: Article

References (26)

1. T. Asano and T. Tokuyama, "Topological Walk Revisited," Proc. Sixth CCCG, 1994.
2. A. Blumer, A. Ehrenfeucht, D. Haussler, and M. Warmuth, "Learnability and the Vapnik-Chervonenkis Dimension," J. ACM, vol. 36, pp. 929-965, 1989.
3. L. Breiman, "Bagging Predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, Aug. 1996.
4. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees. Wadsworth, 1984.
5. N.A.R. Center, "Introduction to IND Version 2.1,"GA23-2475-02, 1992.
6. D. Dobkin, H. Edelsbrunner, and C. Yap, "Probing Convex Polytopes," Proc. 18th ACM Symp. Theory of Computing, pp. 387-392, 1986.
7. Y. Freund and R.E. Schapire, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," J. Computer and System Sciences, vol. 55, no. 1, pp. 119-139, Aug. 1997.
8. T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama, "Constructing Efficient Decision Trees by Using Optimized Association Rules," Proc. 22nd VLDB Conf., pp. 146-155, 1996.
9. T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama, "Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization," Proc. ACM SIGMOD Conf. Management of Data, pp. 13-23, June 1996.
10. T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama, "Finding Optimal intervals Using Computational Geometry," Proc. Int'l Symp. Algorithm and Computing '96, pp. 55-64, 1996.
11. T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama, "Mining Optimized Association Rules for Numeric Attributes," Proc. 15th ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems, pp. 182-191, 1996.
12. T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama, "Mining Optimized Association Rules for Numeric Attributes," J. Computer and System Sciences, 1999.
13. J. Gehrke, R. Ramakrishnan, and V. Ganti, "RainForest - A Framework for Fast Decision Tree Construction of Large Datasets," Proc. 24th Int'l Conf. Very Large Data Bases, pp. 416-427, 1998.
14. S. Hasegawa, H. Imai, and M. Ishiguro, "∈-Approximations of k-Label Spaces," Theoretical Computer Science, vol. 137, pp. 145-157, 1995.
15. D. Haussler and E. Welzl, "Epsilon-Nets and Simplex Range Queries," Discrete and Computational Geometry, vol. 2, pp. 127-151, 1987.
16. M. Mehta, R. Agrawal, and J. Rissanen, "SLIQ: A Fast Scalable Classifier for Data Mining," Proc. Fifth Int'l Conf. Extending Database Technology, pp. 18-32, 1996.
17. Y. Morimoto, T. Fukuda, S. Morishita, and T. Tokuyama, "Implementation and Evaluation of Decision Trees with Range and Region Splitting," Constraint, vol. 2, no. 3/4, pp. 401-427, Dec. 1997.
18. Y. Morimoto, H. Ishii, and S. Morishita, "Efficient Construction of Regression Trees with Range and Region Splitting," Proc. 23nd VLDB Conf., pp. 166-175, 1997.
19. P.M. Murphy and M.J. Pazzani, "Id2-of-3: Constructive Induction of m-of-n Concepts for Discriminators in Decision Trees," Proc. Eighth Int'l Workshop Machine Learning, pp. 183-187, 1991.
20. Z. Pawlak, J. Grzymala-Busse, R. Slowinski, and W. Ziarko, "Rough Sets," Comm. ACM, vol. 38, no. 11, Nov. 1995.
21. F.P. Preparata and M.I. Shamos, Computational Geometry, An Introduction. Springer-Verlag, 1985.
22. J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
23. J.R. Quinlan, "Bagging, Boosting, and C4.5," Proc. 13th Nat'l Conf. Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conf., pp. 725-730, 1996.
24. R. Rastogi and K. Shim, "PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning," Proc. 24th Int'l Conf. Very Large Data Bases, pp. 404-415, 1998.
25. J. Shafer, R. Agrawal, and M. Mehta, "SPRINT: A Scalable Parallel Classifier for Data Mining," Proc. 22nd Int'l Conf Very Large Data Bases, pp. 544-555, 1996.
26. K. Yoda, T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama, "Computing Optimized Rectilinear Regions for Association Rules," Proc. KDD'97, pp. 96-103, Aug. 1997.