Mining frequent itemsets without support threshold: With and without item constraints

IEEE Transactions on Knowledge and Data Engineering

Volumn 16, Issue 9, 2004, Pages 1052-1069

  Cheung, Yin Ling ^a   Fu, Ada Wai Chee ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Association rules; FP tree; Item constraints; N most interesting itemsets

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; KNOWLEDGE ACQUISITION; OPTIMIZATION; PROBLEM SOLVING; TREES (MATHEMATICS);

ASSOCIATION RULES; FP-TREE; ITEM CONSTRAINTS; N-MOST INTERESTING ITEMSETS;

DATABASE SYSTEMS;

EID: 4544293405     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2004.44     Document Type: Article

References (26)

1. C.C. Aggarwal and P.S. Yu, "Mining Large Itemsets for Association Rules," Bull. IEEE Computer Soc. Technical Committee on Data Eng., pp. 23-31, Mar. 1998.
2. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, "Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications," Proc. ACM SIGMOD Conf. Management of Data, 1998.
3. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," Proc. 20th Int'l Conf. Very Large Databases (VLDB), pp. 487-499, 1994.
4. S. Brin, R. Motwani, J.D. Ullman, and S. Tsur, "Dynamic Itemset Counting and Implication Rules for Market Basket Data," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 255-264, 1997.
5. IBM Almaden Research Center, Synthetic Data Generation Code for Associations and Sequential Patterns, http:// www.almaden.ibm.com/software/ quest/.
6. Y.L. Cheung and A. Fu, "An FP-Tree Approach for Mining N-Most Interesting Itemsets," Proc. SPIE Conf. Data Mining, 2002.
7. A.W.-C. Fu, R.W.-W. Kwong, and J. Tang, "Mining N-Most Interesting Itemsets," Proc. Int'l Symp. Methodologies for Intelligent Systems (ISMIS), 2000.
8. G. Grahne, L. Lakshmanan, and X. Wang, "Efficient Mining of Constrained Correlated Sets," Proc. Int'l Conf. Data Eng. (ICDE), 2000.
9. J. Han, J. Pei, and Y. Yin, "Mining Frequent Patterns without Candidate Generation," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2000.
10. Minnesota Population Center in Univ. of Minnesota IPUMS-98, http://www.ipums.umn.edu/usa/samples.html.
11. M. Kamber, J. Han, and J.Y. Chiang, "Mining Partial Periodicity Using Frequent Pattern Trees," Proc. Conf. Knowledge Discovery and Data Mining (KDD), pp. 207-210, 1997.
12. M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo, "Finding Interesting Rules from Large Sets of Discovered Associated Rules," Proc. Int'l Conf. Information and Knowledge Management (CIKM), pp. 401-408, 1994.
13. B. Lent, A. Swami, and J. Widom, "Clustering Association Rules," Proc. Int'l Conf. Data Eng. (ICDE), pp. 220-231, 1997.
14. C. Leung, L. Lakshmanan, and R. Ng, "Exploiting Succinct Constraints Using FP-Trees," ACM SIGKDD Explorations (special issue on constraints in data mining), vol. 4, no. 1, pp. 40-49, June 2002.
15. J.S. Park, M.S. Chen, and P.S. Yu, "An Effective Hash-Based Algorithm for Mining Association Rules," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 175-186, 1995.
16. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, "Discovering Frequent Closed Itemsets for Association Rules," Proc. Seventh Int'l Conf. Database Theory (ICDT), 1999.
17. J. Pei and J. Han, "Constrained Frequent Pattern Mining:. A Pattern-Growth View," Proc. ACM SIGKDD Explorations (special issue on constraints in data mining), vol. 4, no. 1, pp. 31-39, June 2002.
18. J. Pei, J. Han, and R. Mao, "Closet: An Efficient Algorithm for Mining Frequent Closed Itemsets," Proc. ACM SIGMOD Workshop Research Issues in Data Mining and Knowledge Discovery (DMKD'00), May 2000.
19. J. Pei, J. Han, and L.V.S. Lakshmanan, "Mining Frequent Itemsets with Convertible Constraints," Proc. Int'l Conf. Data Eng. (ICDE), 2001.
20. A. Savasere, E. Omiecinski, and S. Navathe, "An Efficient Algorithm for Mining Association Rules in Large Database," Proc. 21st Int'l Conf. Very Large Databases (VLDB), pp. 432-443, 1995.
21. P. Shenoy, J.R. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, and D. Shah, "Turbo-Charging Vertical Mining of Large Databases," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 1-12, 2000.
22. C. Silverstein, S. Brin, R. Motwani, and J Ullman, "Scalable Techniques for Mining Causal Structures," Proc. 24th Int'l Conf Very Large Databases (VLDB), pp. 594-605, 1998.
23. R. Srikant, Q. Vu, and R. Agrawal, "Mining Association Rules with Item Constraints," Proc. Conf. Knowledge Discovery and Data Mining (KDD), pp. 67-73, 1997.
24. K. Wang, Y. He, and J. Han, "Pushing Support Constraints into Frequent Itemset Mining," School of Computing, National Univ. of Singapore, 2000.
25. K. Wang, Y. He, and J. Han, "Mining Frequent Itemsets Using Support Constraints," Proc. 26th Very Large Databases (VLDB) Conf., 2000.
26. Z. Zheng, R. Kohavi, and L. Mason, "Real World Performance of Association Rule Algorithms," Proc. Knowledge Discovery and Data Mining (KDD), pp. 401-406, 2001.