On data structures for association rule discovery

Applied Artificial Intelligence

Volumn 21, Issue 2, 2007, Pages 57-79

  Yan, Xiaowei ^a   Zhang, Shichao ^a,b   Zhang, Chengqi ^b  

^a GUANGXI NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; COSTS; DATA MINING; TREES (MATHEMATICS);

ASSOCIATION RULE DISCOVERY; ASSOCIATION RULE MINING; COMPUTATION COSTS; HASH TREE;

DATA STRUCTURES;

EID: 33847271719     PISSN: 08839514     EISSN: 10876545     Source Type: Journal    
DOI: 10.1080/08839510601115544     Document Type: Article

References (24)

1. Agarwal, R., C. Aggarwal, and V. Prasad. 2000. Depth first generation of long patterns. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 108-118, Boston, Massachusetts, USA.
2. Agarwal, R., C. Aggarwal, and V. Prasad, 2000, A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing, Special Issue on High Performance Data Mining 61(3):350-371.
3. Agrawal, R., T. Imielinski, and A. Swami, 1993. Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD International Conference on Management of Data (SIGMOD'93), pages 207-216, Washington, D.C. USA.
4. Agrawal, R., H. Mannila, R. Srikant, H. Toivonen, and A. Verkamo. 1996. Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, eds. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 307-328. Menlo Park, CA: AAAI/MIT Press.
5. Aho, A., J. Hopcroft, and J. Ullman. 1976. The Design and Analysis of Computer Algorithms. Massachusetts: Addison-Wesley.
6. Blake, C. and C. Merz. 1998. UCI Repository of Machine Learning Databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
7. Brin, S., R. Motwani, and C. Silverstein. 1997. Beyond market baskets: Generalizing association rules to correlations. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'97), pages 265-276, Tucson, Arizona, USA.
8. Brin, S., R. Motwani, J. Ullman, and S. Tsur. 1997. Dynamic itemset counting and implication rules for market basket data. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'97), pages 255-264, Tucson, Arizona, USA.
9. Burdick, D., M. Calimlim, and J. Gehrke. 2001. MAFIA: A maximal frequent itemset algorithm for transactional databases. In Proceedings of the 17th International Conference on Data Engineering, pages 443-452, Heidelberg, Germany.
10. El-Hajj, M. and O. Zaane. 2003. Inverted matrix: Efficient discovery of frequent items in large datasets in the context of interactive mining. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), Washington D.C., USA.
11. Han, J. and Y. Fu. 1995. Discovery of multiple level association rules from large databases. In Proceedings of 21st International Conference on Very Large Databases (VLDB'95), pages 420-431, Zurich, Switzerland.
12. Han, J., J. Pei, and Y. Yin. 2000. Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 1-12, Dallas, Texas, USA.
13. Huang, H., X. Wu, and R. Relue. 2002. Association analysis with one scan of databases. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), pages 629-631, Maebashi City, Japan.
14. Liu, J., Y. Pan, K. Wang, and J. Han. 2002. Mining frequent item sets by opportunistic projection. In Proceedings of the Eighth ACM SIGKDD, pages 229-238, Edmonton, Alberta, Canada.
15. Park, J., M. Chen, and P. Yu. 1995. An effective hash based algorithm for mining association rules. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'95), pages 175-186, San Jose, California, USA.
16. Pei, J., J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. 2001. H-Mine: Hyper-structure mining of frequent patterns in large databases. In Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM'01), pages 441-448, San Jose, California, USA.
17. Savasere, A., E. Omiecinski, and S. Navathe. 1995. An efficient algorithm for mining association rules in large databases. In Proceedings of the 21st International Conference on Very Databases (VLDB'95), pages 432-444, Zurich, Switzerland.
18. Sucahyo, Y. and R. Gopalan. 2003. CT-ITL: Efficient frequent item set mining using a compressed prefix tree with pattern growth. In Proceedings of the 14th Australasian Database Conference (ADC2003), pages 95-104, Adelaide, Australia.
19. Tan, P., V. Kumar, and J. Srivastava. 2002. Selecting the right interestingness measure for association patterns. In Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining, pages 32-41, Edmonton, Alberta, Canada.
20. Toivonen, H. 1996. Sampling large databases for association rules. In Proceedings of the 22nd International Conference on Very Large Databases (VLDB'96), pages 134-145, Mumbay, India.
21. Xu, Y., J. Yu, G. Liu, and H. Lu. 2002. From path tree to frequent patterns: A framework for Mining Frequent Patterns. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), pages 514-521, Maebashi City, Japan.
22. Yan, X., C. Zhang, and S. Zhang. 2003. A database-independent approach of mining association rules with genetic algorithm. In Proceedings of the Fourth International Conference on Intelligent Data Engineering and Automeated Learning(IDEAL), pages 882-886, Hong Kong.
23. Yan, X., C. Zhang, and S. Zhang. 2004. Identifying software component association with genetic algorithm. International Journal of Software Engineering and Knowledge Engineering: Special Issue on Data Mining for Software Engineering and Knowledge Engineering, (accepted, forthcoming).
24. Zaki, M., S. Parthasarathy, M. Ogihara, and W. Li. 1997. New algorithms for fast discovery of association rules. In Proceedings of the 3rd International Conference on Knowledge Discovery in Databases (KDD'97), pages 283-286, Newport Beach, California, USA.