An efficient association rule mining algorithm and business application

ICCCAS 2007 - International Conference on Communications, Circuits and Systems 2007

Volumn , Issue , 2008, Pages 959-965

  Zhang, Zheng ^a   Wang, Hui Wen ^a  

^a BEIHANG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPETITIVE INTELLIGENCE; DATA MINING; DATABASE SYSTEMS; PROBLEM SOLVING;

APRIORI ALGORITHM; BUSINESS APPLICATION; TRANSACTIONAL DATABASE;

ASSOCIATION RULES;

EID: 40649111189     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. R.Agrawal, T.Imielinski, and A.Swami. Mining association rules between sets of items in large database. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 207-216,1993.
2. R.Agrawal and R.Srikant. Fast algorithms for mining association rule. In 20th International Conference on Very Large Databases, Santiago,Chile pages 487-499,September 1994.
3. G.Webb, Efficient search for association rules. In Proceedings of Iternational Conference on Knowledge Discovery and DataMining pages 99-107,2000.
4. A.Savasere, E.Omiecinski, and S.Navathe. An efficient algorithm for mining association rules in large databases. In Very Large Databases, pages 432-443, 1995.
5. J.Han, J.Pei, and Y.Yin. Mining frequent patterns without candidate gereration. In ACM-SIGMOD. Dallas, 2000
6. S.Brin, R.Motwani and C.Silverstein, Beyond market baskets: generalizing association rules to correlations. In: Proceedings of the ACM SIGMOD Conference on Management of Data, pages 255-264, 1997.
7. R.Agarwal, C.Aggarwal, and V. V.V.Prasad. A tree projection algorithm for generation of frequent itemsets. In J. Parallel and Distributed Computing,2000
8. M.Ei-hajj and O.Zaiane. Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining. In Proceedings of Iternational Conference on Knowledge Discovery and DataMining pages 109-118,2003.
9. R.Srikant and R.Agrawal. Mining quantitative association rules in large ralational tables. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 1-12,1996.
10. R.Rastogi and K.Shim, Mining Optimized association rules with categoriacl and numeric attributes. In Proceedings of Iternational Conference on Data Engineering,pages 503-512, 1998
11. R.Srikant and R.Agrawal. Mining Sequential Patterns: Generalizations and performance Improvements. In Proceedings of Iternational Conference on Extending Database Technology,pages 3-17,1996
12. G.Das, K.Lin, H.Mannila, G.Renganathan and P.Smyth. Rule discover form time series. In Proceedings of knowledge discovery in databases. Pages 16-22,1998.
13. L.Feng, T.Dillon and J.Liu. Inter-transactional association rules for multi-dimensional contexts for prediction and their application to studying meteorological data. In Data and Knowledge Engineering.pages 85-115, 2001
14. L.Lakshmanna, R.Ng, J.Han and A.Pang. Optimization of constrained frequent set queries with 2-variable constraints. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 157-168,1999
15. R.Bayardo, R.Agrawal. Constraint-based rule mining in large database. In Proceedings of Iternational Conference on Data Engineering,pages 188-197,1999.
16. C.Zhang and S.Zhang. Association rule mining: Models and Algorithems. Springer press, 2002
17. M.Chen, J.Han and P.Yu. Data mining: an overview from a database perspective, In IEEE Trans. Knowledge and Data Eng. Pages 866-881,1996.
18. M.J.Zaki and C.J.Hsiao.Charm: An efficient algorithm for closed itemset mining. In Proceedings of 2th SIAM International Conference on Data Mining,pages 457-473,2002
19. http://www.netnam.vn/unescocourse/knowledge.htm
20. http://www.dmresearch.net/