Abstract
The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) it should be the minimal and the simplest association rule set; 2) its predictive power should in no way be weaker than that of the complete association rule set so that the precision of the association rule set analysis can be guaranteed. By adopting the least association rule set, the pruning of weak rules can be effectively carried out so as to greatly reduce the number of frequent itemset, and therefore improve the mining efficiency. Finally, based on the classical Apriori algorithm, the upward closure property of weak rules is utilized to develop a corresponding efficient algorithm.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
HAN Jia-wei, Kamber M, Data Mining: Concepts and Techniques[M]. Beijing: China Machine Press, 2001. (in Chinese)
LI Jiu-yong, HONG Shen, RODNEY T. Mining the optimal class association rule set[J]. Knowledge-based System, 2002,15(7): 399–405.
CHEN Guo-qing, WEI Qiang, LIU De, et al. Simple association rules (SAR) and the SAR-based rule discovery [J]. Computers & Industrial Engineering, 2002, 43(4): 721–733.
Agrawal R, Mannila H, Srikant R, et al. Fast discovery of association rules[A]. Advances in Knowledge Discovery and Data Mining [C]. Cambridge: MIT Press, 1996.
Holt J D, Chung S M. Mining association rules using inverted hashing and pruning[J]. Information Processing Letters, 2002, 83(4): 211–220.
Sarawagi S, Thomas S, Agrawal R. Integrating association rule mining with relational database systems: alternatives and implications[A]. Proceedings of the Fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining [C]. New York: ACM Press, 1998.
Takahara Y, CHEN Xiao-hong. Contribution of mathematical general systems theory to organization theory: integration of organizational behaviors on macro and micro levels[A]. Cybernetics and Systems 15th European Meeting on Cybernetics and Systems Research [C]. Vienna University Press, 2000.
Takahara Y, Shiba N, LIU Yong-mei. General system theoretic approach to data mining system[J]. International Journal of General Systems, 2002, 31(3): 245–264.
CHEN Xiao-hong. Theory and application of decision support systems [M]. Beijing: Tsinghua University Publication House, 2000. (in Chinese)
CHEN M, HAN J, YU P. Data mining: an overview from a database perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6): 866–881.
ZHANG S. Aggregation and maintenance for database mining[J]. Intelligent Data Analysis, 1999, 3(6): 475–490.
SHEN Lia, SHEN Hongb, CHENG Lin-ga. New algorithms for efficient mining of association rules[J]. Information Sciences, 1999, 118(1–4):251–268.
Bayardo R, Agrawal R. Mining the most interesting rules[A]. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C]. New York: ACM Press, 1999, 8(15–18):145–154.
Zaki M J S, Ogihara P M. New algorithms for fast discovery of association rules[R]. Technical Report 651. Computer Science Department, University of Rochester, 1997.
Nicolasa P, Yvesa B, Rafika T. Efficient mining of association rules using closed itemset lattices[J]. Information Systems, 1999, 24(1): 25–46.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: Project (70125002) supported by National Science Fund for Distinguished Young Scholars of National Natural Science Foundation of China
Rights and permissions
About this article
Cite this article
Chen, Xh., Lai, Bc. & Luo, D. Mining association rule efficiently based on data warehouse. J Cent. South Univ. Technol. 10, 375–380 (2003). https://doi.org/10.1007/s11771-003-0042-6
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s11771-003-0042-6