Partitioned approach to association rule mining over multiple databases

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

Volumn 3181, Issue , 2004, Pages 320-330

  Kona, Himavalli ^a   Chakravarthy, Sharma ^a  

^a UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION RULES; DATA WAREHOUSES; DATABASE SYSTEMS; INFORMATION MANAGEMENT;

DATA-BASE MANAGEMENT SYSTEMS; IMPROVING PERFORMANCE; JOIN ALGORITHM; MAIN MEMORY; PARTITIONED DATABASE;

DATA MINING;

EID: 35048863497     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30076-2_32     Document Type: Article

References (17)

1. Zhang, S., X. Wu, and C. Zhang, Multi-Database Mining. IEEE Computational Intelligence Bulletin, Vol. 2, No. 1, 2003: p. 5-13.
2. Wu, X. and S. Zhang. Synthesizing High-Frequency Rules front Different Data Sources, in IEEE TKDE 2003.
3. Thomas, S., Architectures and optimizations for integrating Data Mining algorithms with Database Systems, Ph.D Thesis, CISE. Department 1998, University of Florida, Gainesville.
4. Thuraisingham, B., A Primer for Understanding and Applying Data Mining. IEEE, 2000. Vol. 2, No.1: p. 28-31.
5. Agrawal, R., T. Imielinski, and A. Swami. Mining Association Rules between sets of items in large databases, in Proc. of ACM SIGMOD 1993.
6. Agrawal, R. and R. Srikant. Fast Algorithms for mining association rules, in 20th Int'l Conference on Very Large Databases,. 1994.
7. Savascre, A., E. Omiecinsky, and S. Navathe. An efficient algorithm for mining association rules in large databases, in 21st Int'l Cong. on Very Large Databases (VLDB). 1995. Zurich, Switzerland.
8. Houtsma, M. and A. Swami. Set-Oriented Mining for Association Rules in Relational Databases, in Proc. of ICDE, 1995.
9. Meo, R., G. Psaila, and S. Ceri. A New SQL-like Operator for Mining Association Rules, in Proc. of VLDB 1996. Mumbai, India.
10. Agrawal, R. and K. Shim, Developing tightly-coupled Data Mining Applications on a Relational Database System. 1995, IBM Almaden Research Center: San Jose, California.
11. Sarawagi, S., S. Thomas, and R. Agrawal. Integrating Association Rule Mining with Relational Database System: Alternatives and Implications, in ACM SIGMOD 1998. Seattle, Washington.
12. Hongen, Z., Mining and Visualization of Association Rules over Relational DBMSs, MS Thesis CISE Department 2000, UFL, Gainesville.
13. Dudgikar, M., A Layered Optimizer or Mining Association Rules over RDBMS, MS Thesis, CISE Dept. 2000, University of Florida, Gainesville.
14. Mishra, P. and S. Chakravarthy, Performance Evaluation and Analysis of SQL-92 Approaches for Association Rule Mining in Proc. of DA WAK 2003.
15. Toivonen, H. Sampling Large Databases for Association Rules. In Proc. of VLDB. 1996.
16. Mishra, P. and S. Chakravarthy, Evaluation of K-way Join and its variants for Association Rule Mining in Proc. of BNCOD 2002.
17. Kona, H, Association Rule Mining Over Multiple Databases: Partitioned and Incremental Approaches, MS Thesis, UTA, Fall 2003 http://www.cse.uta.edu/ Research/Publications/Downloads/CSE-2003-40.pdf