Improved Apriori Algorithm in the Power System Fault

Abstract:

For the low efficiency in generating candidate item sets of apriori algorithm, this paper presents a method based on property division to improve generating candidate item sets. Comparing the improved apriori algorithm with the other algorithm and the improved algorithm is applied to the power system accident cases in extreme climate. The experiment results show that the improved algorithm significantly improves the time efficiency of generating candidate item sets. And it can find the association rules among time, space, disasters and fault facilities in the power system accident cases in extreme climate. That is very useful in power system fault analysis.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 1510-1514
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.1510
• Online since: May 2014
• Authors: Li Qiang Lin*, Hong Wen Yan
• Keywords: Apriori Algorithm, Association Rule, Extreme Climate, Power System

References

[1] Agrawal R, Srikant R. Mining association rules between sets of items in large databases [A]. Proc ACM SIGMOD Int l Conf Management of data [C]. Washington DC, May 1993. 207~ 216.

DOI: 10.1145/170036.170072

[2] Chen Xiang, Wu Yue. Association Rule Mining Algorithm Based on Base Set and Concept Lattice [J]. Computer Engineering, 2010, 36 (19) : 34-36.

[3] Liu Huating, Guo Renxiang, Jiang Hao. Research and improvement of Apriori algorithm for mining association rules[J]. Computer Applications and Software, 2009, 26 (1) : 146-149.

[4] He Bo. Distributed algorithm for mining association rules based on FP-tree [J]. Control and Decision, 2012, 27(4) : 618-622.

[5] TOLVONEN H. Sampling large databases for association rules[C]/ Proceedings of the 22nd International Conference on Very Large Database. Bombay, India [s. n. ], 1996: 134- 145.

[6] BRIN S. Dynamic item set counting and implication rules for market basket analysis [C]/Proceedings of International Conference on the Special Interest Group on Management of Data. New York ACM, 1997: 255- 264.

DOI: 10.1145/253262.253325

[7] ERNESTO V M , OSCAR L , CHACON M et al . An On-line Expert System for Fault Section Diagnosis in power systems. IEEE Trans on Power Systems , 1997, 12(1): 357-362.

DOI: 10.1109/59.574959

[8] Yang Jianwei, He Zhengyou. Power system fault diagnosis approach based on time sequence fuzzy Petri net [J]. Automation of electric power systems, 2011, 35 (15) : 32-35.

[9] Lu Peng, Wang Xihuai， Xiao Jianmei. Method of fault diagnosis in power system based on rough set theory and graph theory [J]. Control and Decision, 2013, 28 (4) : 511-516.

[10] Hu Haibo, Wang Yingchun, Xiong Yajun. AHP model-based risk assessment of lightning in Beijing [J]. Journal of Natural Disasters, 2010, 19 (1): 104-109.

[11] Li Dengwen, Yang jing, Wu Xingyang. Discussion on Genesis of Wire Icing in Guizhou During the Freezing Rain and Snow Storm in January 2008[J]. Meteorological Monthly, 2011 5 (2) : 161-169.

[12] Fu GuiQin, Cao Xin. The characteristic analysis of Thunderstorm Gale and Electrical Network Disaster in HeBei [J]. Meteorological Monthly, 2012, 20 (3) : 353-357.