Computing Optimized Rectilinear Regions for Association Rules

Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

Volumn , Issue , 1997, Pages 96-103

  Yoda, Kunikazu ^a   Fukuda, Takeshi ^a   Morimoto, Yasuhiko ^a   Morishita, Shinichi ^a   Tokuyama, Takeshi ^a  

^a IBM RESEARCH TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ASSOCIATION RULES; DECISION TREES;

EXPERIMENTAL TEST; PROPERTY; TEST DATA; TRAINING DATABASE; TRAINING DATASET; TWO-DIMENSIONAL; VERTICAL LINES;

STATISTICAL TESTS;

EID: 85027099726     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Agrawal, R., and Srikant, R. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference, 487-499.
2. Agrawal, R.; Imielinski, T.; and Swami, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of the A CM SIGMOD Conference on Management of Data, 207-216.
3. Apte, C; Hong, S. J.; Prasad, S.; and Rosen, B. 1995. Ramp: Rules abstraction for modeling and prediction. Technical Report RC-20271, IBM Watson Research Center.
4. Asano, T.; Chen, D.; Katoh, N.j and Tokuyama, T. 1996. Polynomial-time solutions to image segmentations. In Proc. 7th A CM-SIAM Symposium on Discrete Algorithms, 104-113.
5. Breiman, L.; Friedman, J. H.; OIshen, R. A.; and Stone, C. J, 1984. Classification and Regression Trees. Wadsworth.
6. Fukuda, T.; Morimoto, Y.; Morishita, S.; and Tokuyama, T. 1996a. Mining optimized association rules for numeric attributes. In Proceedings of the Fifteenth A CM SIGA CT-SIGMOD-SIGART Symposium on Principles of Database Systems, 182-191.
7. Fukuda, T.; Morimoto, Y.; Morishita, S.; and Tokuyama, T. 1996b. Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization. In Proceedings of the ACM SIGMOD Conference on Management of Data, 13-23.
8. Fukuda, T.; Morimoto, Y.; Morishita, S.; and Tokuyama, T. 1996c. Constructing efficient decision trees by using optimized association rules. In Proceedings of the 22nd VLDB Conference, 146-155.
9. Fukuda, T.; Morimoto, Y.; Morishita, S.; and Tokuyama, T. 1997. Implementation and evaluation of decision trees with range and region splitting. Technical Report RT0202, IBM Tokyo Research Laboratory.
10. Keim, D.; Kriegel, H.; and Seidl, T. 1994. Supporting data mining of large database by visual feedback queries. In Proc. 10th Data Enginieering, 302-313. Mehta, M.; Agrawal, R.; and Rissanen, J. 1996. Sliq: A fast scalable classifier for data mining. In Proceedings of the Fifth International Conference on Extending Database Technology.
11. Ng, R. T., and Han, J. 1994. Efficient and effective clustering methods for spatial data mining. In Proceedings of the 20th VLDB Conference, 144-155. Park, J. S.; Chen, M.-S.; and Yu, P. S. 1995. An effective hash-based algorithm for mining association rules. In Proceedings of the ACM SIGMOD Conference on Management of Data, 175-186.
12. Piatetsky-Shapiro, G. 1991. Discovery, analysis, and presentation of strong rules. In Knowledge Discovery in Databases, 229-248.
13. Quintan, J. R. 1986. Induction of decision trees. Machine Learning 1:81-106.
14. Quintan, J. R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann.
15. Srikant, R., and Agrawal, R. 1996. Mining quantitative association rules in large relational tables. In Proceedings of the ACM SIGMOD Conference on Management of Data.
16. Yoda, K.; Fukuda, T.; Morimoto, Y.; Morisita, S.; and Tokuyama, T. 1997. Computing optimized rectilinear regions for association rules. Technical Report RT0201, IBM Tokyo Research Laboratory.
17. Zhang, T.; Ramakrishnan, R.; and Linvy, M. 1996. Birch: An efficient data clustering method for very large databases. In Proceedings of the A CM SIGMOD Conference on Management of Data, 103-114.