Novel alarm correlation analysis system based on association rules mining in telecommunication networks

Information Sciences

Volumn 180, Issue 16, 2010, Pages 2960-2978

  Li, Tongyan ^a,b   Li, Xingming ^a  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b CHENGDU UNIVERSITY OF INFORMATION TECHNOLOGY   (China)

Author keywords

Alarm correlation analysis; Association rules mining; Neural network; Weighted frequent pattern tree; Weighted potential frequent itemsets

Indexed keywords

ALARM CORRELATION ANALYSIS; ASSOCIATION RULES MINING; FREQUENT ITEMSETS; OPTIMIZATION TECHNIQUES; REAL-WORLD DATASETS; RESEARCH AREAS; ROOT CAUSE; WEIGHTED FREQUENT PATTERN TREE;

ALARM SYSTEMS; ASSOCIATION RULES; ASSOCIATIVE PROCESSING; CORRELATION METHODS; DATA MINING; MINING; TELECOMMUNICATION NETWORKS; TREES (MATHEMATICS);

NEURAL NETWORKS;

EID: 78049238133     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2010.04.013     Document Type: Article

References (30)

1. R. Agrawal, R. Srikant, Fast algorithm for mining association rules, in: Proceedings of the 20th VLDB Conference, 1994, pp. 487-499.
2. N. Amani, M. Fathi, M. Dehghan, A case-based reasoning method for alarm filtering and correlation in telecommunication networks, in: Proceedings of the Canadian Conference on Electrical and Computer Engineering, 2005, pp. 2182-2186.
3. Y.J. Bei, G. Chen, L. Shou, X.Y. Li, and J.X. Dong Bottom-up discovery of frequent rooted unordered subtrees Information Sciences 179 1 2009 70 88
4. E. Cohen, M. Datar, S. Fujiwara, A. Gionis, P. Indyk, R. Motwani, J.D. Ullman, and C. Yang Finding interesting associations without support pruning IEEE Transactions on Knowledge and Data Engineering 13 1 2001 64 78
5. C.H. Cai, Ada W.C. Fu, C.H. Cheng, W.W. Kwong, Mining association rules with weighted items, in: Proceedings of IEEE International Database Engineering and Applications Symposium, 1998, pp. 68-77.
6. Q. Ding, Q. Ding, and W. Perrizo PARM - an efficient algorithm to mine association rules from spatial data IEEE Transactions on Systems, Man, and Cybernetics 38 6 2008 1513 1524
7. B. Elena, C. Tania, and C. Silvia IMine: index support for itemset mining IEEE Transactions on Knowledge and Data Engineering 21 4 2009 493 506
8. G. Grahne, J. Zhu, High performance mining of maximal frequent itemsets, in: Proceedings of the Sixth SIAM International Workshop on High Performance Data Mining, 2003, pp. 135-143.
9. G. Grahne, and J. Zhu Fast algorithms for frequent itemset mining using FP-trees IEEE Transactions on Knowledge and Data Engineering 17 10 2003 1347 1362
10. K. Hatonen, M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, TASA: telecommunication alarm sequence analyzer or how to enjoy faults in your network, in: Proceedings of IEEE Network Operations and Management Symposium, 1996, pp. 520-529.
11. K. Hatonen, M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, Knowledge discovery from telecommunication network alarm databases, in: Proceedings of 12th International Conference on Data Engineering, 1996, pp. 115-122.
12. J. Han, J. Pei, Y. Yin, and R. Mao Mining frequent patterns without candidate generation: a frequent-pattern tree approach Data Mining and Knowledge Discovery 8 1 2004 3 87
13. T.M. Hu, S.Y. Sung, H. Xiong, and Q. Fu Discovery of maximum length frequent itemsets Information Sciences 178 1 2008 69 87
14. S.Z. Hou, X.F. Zhang, Analysis and research for network management alarms correlation based on sequence clustering algorithm, in: Proceedings of ICICTA'2008, 2008, pp. 982-986.
15. J.W. Han, Y.J. Fu, Discovery of multiple-level association rules from large databases, in: Proceedings of the 1995 International Conference on Very Large Data Bases, 2002, pp. 420-431.
16. M. Klemettinen, H. Mannila, and H. Toivonen Rule discovery in telecommunication alarm data Network and Systems Management Journal 7 4 1999 395 423
17. Y. Li, S. Zhu, X.S. Wang, and S. Jajodia Looking into the seeds of time: discovering temporal patterns in large transaction sets Information Sciences 176 8 2006 1003 1031
18. Z.C. Li, P.L. He, M. Lei, A high efficient aprioritid algorithm for mining association rule, in: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, 2005, pp. 18-21.
19. A.J.T. Lee, and C.S. Wang An efficient algorithm for mining frequent inter-transaction patterns Information Sciences 177 17 2007 3453 3476
20. H. Mannila, H. Toivonen, and A.I. Verkamo Discovery of frequent episodes in event sequences Data Mining and Knowledge Discovery 1 1997 259 289 (Pubitemid 127721244)
21. R. Ng, L.V.S. Lakshmanan, J. Han, A. Pang, Exploratory mining and pruning optimizations of constrained association rules, in: Proceedings of SIGMOD'98, 1998, pp. 13-24.
22. W. Ouyang, C. Zheng, and O.S. Cai Discovery of weighted association rules in databases Journal of Software 12 4 2001 612 619
23. S. Sarawagi, S. Thomas, R. Agrawal, Integrating association rule mining with relational database system: alternatives and implications, in: Proceedings of SIGMOD'98, 1998, pp. 343-354.
24. R. Srikant, Q. Vu, R. Agrawal, Mining association rules with item constraints, in: Proceedings of KDD'97, 1997, pp. 67-73.
25. M.J. Song, and S. Rajasekaran A transaction mapping algorithm for frequent itemsets mining IEEE Transactions on Knowledge and Data Engineering 18 4 2006 472 481
26. Y.N. Tang, E. Al-Shaer, and R. Boutaba Efficient fault diagnosis using incremental alarm correlation and active investigation for internet and overlay networks IEEE Transactions on Network and Service Management 5 1 2008 36 49
27. F. Tao, F. Murtagh, M. Farid, Weighted association rule mining using weighted support and significance framework, in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, pp. 24-27.
28. T. Wang, P.L. He, Database encoding and an anti-apriori algorithm for association rules mining, in: Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, 2006, pp. 1195-1198.
29. W. Wang, J. Yang, P. Yu, Efficient mining of weighted association rules (WAR), in: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2000, pp. 270-274.
30. U. Yun Efficient mining of weighted interesting patterns with a strong weight and/or support affinity Information Sciences 177 17 2007 3477 3499 (Pubitemid 46920531)