Efficient mining of indirect associations using HI-mine

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2671, Issue , 2003, Pages 206-221

  Wan, Qian ^a   An, Aijun ^a  

^a YORK UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; VIRTUAL REALITY; ALGORITHMS; COMPUTER SCIENCE; DATA STRUCTURES; INFORMATION TECHNOLOGY; SPECIFICATIONS;

INDIRECT ASSOCIATION; INFREQUENT PATTERNS; REAL-WORLD;

ALGORITHMS; DATA MINING;

ASSOCIATION RULES; DATA SETS; HI-MINE ALGORITHM; SUPPORT SPECIFICATIONS;

EID: 7044274055     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44886-1_17     Document Type: Conference Paper

References (16)

1. R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent itemsets. In J. of Parallel and Distributed Computing (Special Issue on High Performance Data Mining), 2000.
2. C. Aggarwal and P. Yu. A new framework for itemset generation. In Proc. of the Fourth Int.l Conference on Knowledge Discovery and Data Mining, pages 129-133, New York, NY, 1996.
3. R. Agrawal and R. Srikant. Fast Algorithms for mining association rules. Proceedings of the 20th Int.l Conference on Very Large Data Bases, pp.487-499, Santiago, Chile (1994).
4. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD int.l Conference on Management of Data, pp. 207-216, Washington D.C., USA (1993).
5. S. Brin, R. Motwani, and C. Silverstein. Beyond market baskets: Generalizing association rules to correlations. In Proc. ACM SIGMOD intl. Conf. Management of Data, pages 265-276, Tucson, AZ, 1997.
6. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation, In SIGMOD.00, pages 1-12.
7. J. S. Park, M. S. Chen, and P. S. Yu. An efficient hash-based algorithm for mining association rules. SIGMOD Record, 25(2):175-186, 1995.
8. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. H-Mine: Hyper- Structure Mining of Frequent Patterns in Large Database.
9. J. Pei, J. Han, and R. Mao. CLOSET: An efficient algorithm for mining frequent closed itemsets. In Proc. 2000 ACM-SIGMOD Int. Workshop Data Mining and Knowledge Discovery (DMKD.00), pages 11-20.
10. P. Tan and V. Kumar. Interestingness measures for association patterns: A perspective. In KDD 2000 Workshop on Postprocessing in Machine Learning and Data Mining, Boston, MA, August 2000.
11. P. N. Tan, and V. Kumar. Mining Indirect Associations in Web Data. In Proc of WebKDD 2001: Mining Log Data Across All Customer TouchPoints, August (2001)
12. P. N. Tan, V Kumar, H Kuno. Using SAS for Mining Indirect Associations in Data, In Proc of the Western Users of SAS Software Conference (2001).
13. P. N. Tan, V. Kumar, and J. Srivastava. Indirect Association: Mining Higher Order Dependences in Data. Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, 632-637, Lyon, France (2000).
14. Savasere, E. Omiecinski, and S. Navathe. Mining for strong negative associations in a large database of customer transactions. In Proc. of the 14th International Conference on Data Engineering, pages 494-502, Orlando, Florida, February 1998.
15. Savaswre, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proc. of the 21st Int. Conf. on Very Large Databases (VLDB.95), Zurich, Switzerland, Sept., 1995.
16. Wong and C. J. Butz. Constructing the Dependency Structure of a Multi-Agent Probability Network. IEEE Transactions on Knowledge and Data Engineering, Vol. 13, No. 3, 395-415, May 2001.