CBW: An efficient algorithm for frequent itemset mining

Proceedings of the Hawaii International Conference on System Sciences

Volumn 37, Issue , 2004, Pages 1039-1048

  Su, Ja Hwung ^a   Lin, Wen Yang ^a  

^a I SHOU UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINT THEORY; DATA MINING; DATA REDUCTION; DATABASE SYSTEMS; DECISION SUPPORT SYSTEMS; INPUT OUTPUT PROGRAMS; REAL TIME SYSTEMS; THRESHOLD ELEMENTS;

ASSOCIATION MINING; FRAUD DETECTION; FREQUENT ITEMSET MINING; TRANSACTION RECORDS;

ALGORITHMS;

EID: 12344281732     PISSN: 10603425     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. R.C. Agarwal, C.C. Aggarwal, and V.V.V. Prasad, "Depth first generation of long patterns," in Proceedings of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000, pp. 108-118.
2. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," in Proceedings of the 20th VLDB Conference, 1994, pp. 487-499.
3. R.J. Bayardo Jr., "Efficiently Mining Long Patterns from Databases," in Proceedings of 1998 ACM SIGMOD International Conference on Management of Data. Seattle, Washington, USA, 1998, pp. 85-93.
4. S. Brin, R. Motwani, J.D. Ullman, and S. Tsur, "Dynamic Itemset Counting and Implication Rules for Market Baseket Data," SIGMOD Record, Vol. 26, 1997, pp. 255-264.
5. Database Research Group in the Department of Computer Science and Engineering at the Chinese University of Hong Kong, http://www.cse.cuhk.edu.hk/ ~kdd/program.html.
6. J. Man, J. Pei, and Y. Yin, "Mining Frequent Patterns Without Candidate Generation," in Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA, 2000, pp. 1-12.
7. J. Hipp, U. Guntzer, and G. Nakhaeizadeh, "Algorithms for Association Rule Mining - A General Survey and Comparison," SIGKDD Explorations, Vol. 2, 2000, pp. 58-64.
8. J. Hipp, U. Guntzer, and G. Nakhaeizadeh, "Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today's Approaches," in Proceedings of 4th European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'00), 2000, pp. 159-168.
9. D. Lin and Z.M. Kedem, "Pincer-search: An Efficient Algorithm for Discovering the Maximum Frequent Set," IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No. 3, 2002, pp. 553-566.
10. W.Y. Lin, J.H. Su and M.C. Tseng, "OMARS: The Framework of an Online Multi-dimensional Association Rules Mining System," in Proceedings of the 2nd International Conference on Electronic Business, Taipei, Taiwan, 2002, pp. 216-225.
11. J.S. Park, M.S. Chen, and P.S. Yu, "An Effective Hash-Based Algorithm for Mining Association Rules," in Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, San Jose, CA, USA, 1995, pp. 175-186.
12. A. Savasere, E. Omiecinski, and S. Navathe, "An Efficient Algorithm for Mining Association Rules in Large Databases," in Proceedings of the 24th VLDB Conference, 1995, pp. 432-444.
13. M.C. Tseng and W.Y. Lin, "Mining Generalized Association Rules with Multiple Minimum Supports," in Proceedings of International Conference on Data Warehousing and Knowledge Discovery, Munich, Germany, 2001, pp. 11-20.
14. M.J. Zaki, "Scalable Algorithms for Association Mining," IEEE Transactions on Knowledge and Data Engineering, Vol. 12, No. 2, 2000, pp. 372-390.