Abstract
In this paper a distributed weight mining algorithm is proposed based on FP-growth. As an important part of network fault management, the association rule takes effect on eliminating redundant alarms and preventing alarm storm. In traditional association rules the importance of each item is seen as equivalent during mining which is not realistic. By considering the different weights of the items, the AHP approach is introduced in the paper. Without any candidate generation process FP-growth performs well in mining alarm records. The distributed architecture of master and slave site can effectively reduced the complexity of the algorithm. The experimental results and comparison with other algorithms prove the validity of this proposed algorithm and good performance of decreasing the run-time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Han, J., Cheng, H., et al.: Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery 15(1), 55–86 (2007)
Sari, B., Sen, T., Engin Kilic, S.: AHP model for the selection of partner companies in virtual enterprises. In: International Conference on Manufacturing Research, vol. 38, pp. 367–376 (2008)
Jian, W., Ming, L.X.: Based on Association Rules Distributed Mining Algorithm for Alarm Correlation in Communication Networks Research. Computer Science 36(11), 204–207 (2009)
Silva, A., Antunes, C.: Pattern Mining on Stars with FP-Growth. In: Torra, V., Narukawa, Y., Daumas, M. (eds.) MDAI 2010. LNCS, vol. 6408, pp. 175–186. Springer, Heidelberg (2010)
Yun, U.: An efficient mining of weighted frequent patterns with length decreasing support constraints. Knowledge-Based Systems 21(8), 741–752 (2008)
Zhang, K.: Application of Based on Association Rules Data Mining in Telecommunication Alarm Management. University of Electronic Science and Technology, Chengdu (2006)
Li, H., Wang, Y., Zhang, D., et al.: PFP: Parallel FP-Growth for Query Recommendation. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 107–114 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, H., Liu, Y., Wang, C. (2011). Research on Association Rule Algorithm Based on Distributed and Weighted FP-Growth. In: Jin, D., Lin, S. (eds) Advances in Multimedia, Software Engineering and Computing Vol.1. Advances in Intelligent and Soft Computing, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25989-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-25989-0_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25988-3
Online ISBN: 978-3-642-25989-0
eBook Packages: EngineeringEngineering (R0)