Weighted Association Rules Mining Algorithm Research

Jun Tan

doi:10.4028/www.scientific.net/AMM.241-244.1598

Paper Titles

The Design and Simulation of IGBT Inverter Buffer Circuit
p.1567

A Real-Time Transient Model of CF6 Turbofan Engine
p.1573

Different Types of Association Rules Mining Review
p.1589

A New Training Algorithm for RBF Neural Network based on Dynamic Fuzzy Clustering
p.1593

Weighted Association Rules Mining Algorithm Research
p.1598

The Research on Recognition of the Irregular Objects
p.1602

Finite-Time H_∞ Consensus of Multi-Agent Systems with a Leader
p.1608

Human Ear Recognition Based on Phase Congruency and Kernel Discriminant Analysis
p.1614

Research on Parameters Optimization Algorithm in Support Vector Machine Based on Immune Memory Clone Strategy
p.1618

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 241-244Weighted Association Rules Mining Algorithm...

Weighted Association Rules Mining Algorithm Research

Abstract:

Aiming at the problem that most of weighted association rules mining algorithms have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, weighted boolean association rules mining algorithm and weighted fuzzy association rules mining algorithm are presented, which use pruning strategy of Apriori algorithm so that improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.

You might also be interested in these eBooks

Industrial Instrumentation and Control Systems

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 241-244)

Pages:

1598-1601

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.241-244.1598

Citation:

Cite this paper

Online since:

December 2012

Authors:

Jun Tan

Keywords:

Anti-Monotonicity, Apriori Algorithm, Weighted Association Rules, Weighted Support

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] R. Agrawal. Mining association rules between sets of items in large databases[C]. in Proc. of the ACM SIGMOD, Washington, DC, USA: ACM Press, 1993, pp.207-216

DOI: 10.1145/170036.170072

Google Scholar

[2] Tao, F., Murtagh, F., Farid, M. Weighted association rule mining using weighted support and significance framework[C]. in Proc. of 9th ACM SIGKDD, Washington, DC, USA: ACM Press, 2003, pp.661-666

DOI: 10.1145/956750.956836

Google Scholar

[3] Wang, W., Yang, J, Yu, P.S. Efficient mining of weighted association rules(WAR)[C]. in Proc. of the KDD, Boston, MA, 2000, pp.270-274

Google Scholar

[4] Cai, C.H., Fu, A.W-C., Cheng, C.H., Kwong, W.W. Mining association rules with weighted items[C]. in Proc. of 1998 Intl. Database Engineering and applications symposium, Cardiff, Wales, UK, 1998, pp.68-77

DOI: 10.1109/ideas.1998.694360

Google Scholar

[5] Cai, C.H., Fu, A.W-C., Cheng, C.H., Kwong, W.W. Mining association rules with weighted items [C]. In: Proceedings of the 1998 International Conference on Database Engineering and Applications Symposium, Cardiff, Wales, UK, 1998: 68-77.

DOI: 10.1109/ideas.1998.694360

Google Scholar

[6] Tao, F., Murtagh, F., Farid, M. Weighted association rule mining using weighted support and significance framework [C]. In: Proceedings of the 9th ACM SIGKDD, Washington, DC, USA, 2003: 661-666.

DOI: 10.1145/956750.956836

Google Scholar