Mining dependence rules by finding largest itemset support quota

Proceedings of the ACM Symposium on Applied Computing

Volumn 1, Issue , 2004, Pages 525-529

  Savinov, Alexandr ^a  

^a FRAUNHOFER IAIS   (Germany)

Author keywords

Data Mining; Dependence Rules; Expected Support; Support Bounding; Support Quota

Indexed keywords

ALGORITHMS; COMPUTER PROGRAMMING LANGUAGES; INFORMATION ANALYSIS; ITERATIVE METHODS; KNOWLEDGE ACQUISITION; PARAMETER ESTIMATION; PROBABILITY; SET THEORY; STATISTICAL METHODS;

DEPENDENCE RULES; SUPPORT BOUNDING; SUPPORT QUOTA;

DATA MINING;

EID: 2442543442     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/967900.968011     Document Type: Conference Paper

References (11)

1. A.A. Freitas, On rule interestingness measures, Knowlege Based Systems 12, 309-315, 1999.
2. R. Agrawal, T. Imielinski, A. Swami. Mining association rules between sets of items in large databases. Proc. of the ACM SIGMOD Conference on Management of Data, Washington, D.C., May 1993, 207-216.
3. B. Liu, L.-P. Ku and W. Hsu, Discovering Interesting Holes in Data, Proceedings of Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 930-935, August 23-29,1997, Nagoya, Japan.
4. A. Savinov, Mining Possibilistic Set-Valued Rules by Generating Prime Disjunctions, Proc. 3rd European Conference on Principles of Data Mining and Knowledge Discovery (PKDD'99), LNCS No. 1704, pp. 536-541.
5. A. Savinov, Mining Interesting Possibilistic Set-Valued Rules, in: Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications (Eds.: Da Ruan and Etienne E. Kerre), Kluwer, 2000, 107-133.
6. S. Brin, R. Motwani, and C. Silverstein, Beyond market basket: Generalizing association rules to correlations, SIGMOD'97, pp. 265-276.
7. C. Silverstein, S. Brin, and R. Motwani, Beyond Market Baskets: Generalizing Association Rules to Dependence Rules, Data Mining and Knowledge Discovery 2(1), 39-68.
8. R. Meo, Theory of dependence values, ACM Transactions on Database Systems, 25(3), 2000, 380-406.
9. T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. Proc. 6th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD'02), LNCS No. 2431, pp. 74-85.
10. Darroch and D. Ratchli, Generalized Iterative Scaling for Log-Linear Models, The Annals of Mathematical Statistics, Vol. 43, No. 5, pp. 1470-1480, 1972.
11. S. Jaroszewicz and D.A. Simovici. Pruning Redundant Association Rules Using Maximum Entropy Principle. Advances in Knowledge Discovery and Data Mining, 6th Pacific-Asia Conference, PAKDD'02, 135-147.