Enhancing Network Alarm Handling Efficiency: An Improved Apriori Algorithm with FCM Approach

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Broadband Cellular Communication
Received Date: 09/18/2024
Acceptance Date: 09/25/2024
Published On: 2024-11-13
First Page: 1
Last Page: 6

Journal Menu

By: Sohan Lal Gupta, Vikram Khandelwal, Vinod Kataria, Arpita Sharma, and Anjali Pandey

1-4 Assistant Professor, Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology Management & Gramothan Jaipur, Rajasthan, India
5- Assistant Professor, Department of Information Technology, Swami Keshvanand Institute of Technology Management Gramothan Jaipur, Rajasthan, India

Abstract

Network alarm systems generate large volumes of alarms, often resulting in redundant or irrelevant data that complicates efficient fault detection and resolution. This paper presents an enhanced approach to network alarm handling by integrating the Apriori algorithm with the Fuzzy C-Means (FCM) clustering method. The Apriori algorithm is used to uncover frequent alarm patterns, while FCM addresses the complexity of overlapping alarm data, enabling the association of alarms with varying degrees of membership in multiple clusters. The proposed method improves the accuracy of alarm correlation, reduces false positives, and enhances the efficiency of fault localization. This method offers multiple advantages, including a reduction in false alarms, better pattern detection, and strengthened network fault diagnosis capabilities. The use of FCM clustering allows for more adaptable and precise alarm grouping, while the enhanced Apriori algorithm improves both the speed and scalability of pattern discovery. The combination of Apriori and FCM demonstrates significant potential for optimizing network alarm management and improving overall system reliability.

Keywords:  Network alarm management, Improved Apriori algorithm, FCM clustering, Alarm correlation analysis, Alarm noise reduction, Real-time alarm processing, Network performance monitoring.

Loading

Citation:

How to cite this article: Sohan Lal Gupta, Vikram Khandelwal, Vinod Kataria, Arpita Sharma, and Anjali Pandey, Enhancing Network Alarm Handling Efficiency: An Improved Apriori Algorithm with FCM Approach. International Journal of Broadband Cellular Communication. 2024; 10(02): 1-6p.

How to cite this URL: Sohan Lal Gupta, Vikram Khandelwal, Vinod Kataria, Arpita Sharma, and Anjali Pandey, Enhancing Network Alarm Handling Efficiency: An Improved Apriori Algorithm with FCM Approach. International Journal of Broadband Cellular Communication. 2024; 10(02): 1-6p. Available from:https://journalspub.com/publication/ijbcc/article=11917

Refrences:

  1. Agarwal R, Singh S, Vats S. Review of parallel apriori algorithm on mapreduce framework for performance enhancement. InBig Data Analytics: Proceedings of CSI 2015 2018 (pp. 403-411). Springer Singapore.
  2. Altay EV, Alatas B. Intelligent optimization algorithms for the problem of mining numerical association rules. Physica A: Statistical Mechanics and its Applications. 2020 Feb 15;540:123142.
  3. Bacchillone T, Donati M, Saponara S, Fanucci L. A flexible home gateway system for telecare of patients affected by chronic heart failure. In2011 5th International Symposium on Medical Information and Communication Technology 2011 Mar 27 (pp. 139-142). IEEE.
  4. Bagui S, Dhar PC. Positive and negative association rule mining in Hadoop’s MapReduce environment. Journal of Big Data. 2019 Dec;6:1-6.
  5. Baker ZK, Prasanna VK. Efficient hardware data mining with the Apriori algorithm on FPGAs. In13th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM’05) 2005 Apr 18 (pp. 3-12). IEEE.
  6. Cai LB, Zhang W, Zhao LX, Yang XB, Chen L. Monitoring and operation analysis on power environment of computer room based on big data. Applied Mechanics and Materials. 2017 May 29;864:258-63.
  7. Chaudhuri S, Ganti V, Kaushik R. A primitive operator for similarity joins in data cleaning. In22nd International Conference on Data Engineering (ICDE’06) 2006 Apr 3 (pp. 5-5). IEEE.
  8. Chung WH, Kao SL, Chang CM, Yuan CC. Association rule learning to improve deficiency inspection in port state control. Maritime Policy & Management. 2020 Apr 2;47(3):332-51.
  9. Harun NA, Makhtar M, Aziz AA, Mohamad M, Zakaria ZA. Implementation of apriori algorithm for a new flood area prediction system. Advanced Science Letters. 2017 Jun 1;23(6):5419-22.
  10. Ji X, Tong W, Ning B, Mason CE, Kreil DP, Labaj PP, Chen G, Shi T. QuaPra: efficient transcript assembly and quantification using quadratic programming with Apriori algorithm. Science China Life Sciences. 2019 Jul;62:937-46.
  11. Koukouli ME, Balis DS, Zyrichidou I, van der A R, Ding J, Hedelt P, Valks P, Fioletov V. Area Sulphur Dioxide Emissions over China Extracted from GOME2/MetopA Observations. InLiving Planet Symposium 2016 Aug (Vol. 740, p. 359).
  12. Arun Kumar P, Agrawal S, Barua K, Pandey M, Shrivastava P, Mishra H. Dynamic rule-based approach for shelf placement optimization using apriori algorithm. InFrontiers in Intelligent Computing: Theory and Applications: Proceedings of the 7th International Conference on FICTA (2018), Volume 2 2020 (pp. 228-237). Springer Singapore.
  13. Li-Rui W, Zhi-Fu W. Implementation on E-commerce Network Marketing Based on WEB Mining Technology. In2015 Seventh International Conference on Measuring Technology and Mechatronics Automation 2015 Jun 13 (pp. 556-560).
  14. Li L, Yin XL, Jia XC, Sobhani B. Day ahead powerful probabilistic wind power forecast using combined intelligent structure and fuzzy clustering algorithm. Energy. 2020 Feb 1;192:116498.
  15. Yuan X. An improved Apriori algorithm for mining association rules. InAIP conference proceedings 2017 Mar 13 (Vol. 1820, No. 1). AIP Publishing.
  16. Mane RV, Ghorpade VR. Predicting student admission decisions by association rule mining with pattern growth approach. In2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT) 2016 Dec 9 (pp. 202-207).
  17. Mondal KC, Nandy BD, Baidya A. A factual analysis of improved python implementation of Apriori algorithm. Methodologies and Application Issues of Contemporary Computing Framework. 2018:139-51.
  18. Nagashree N, Tejasvi R, Swathi KC. An early risk detection and management system for the cloud with log parser. Computers in Industry. 2018 May 1;97:24-33..
  19. Nair H, Neeba EA. Improved fuzzy space-intervals based sequential pattern mining: Technical solution. In2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) 2015 Dec 10 (pp. 1-4). IEEE.
  20. Oum TH, Yu C, Zhang A. Global airline alliances: international regulatory issues. Journal of Air Transport Management. 2001 Jan 1;7(1):57-62.
  21. Pal SK, Talwar V, Mitra P. Web mining in soft computing framework: relevance, state of the art and future directions. IEEE transactions on neural networks. 2002 Sep;13(5):1163-77.
  22. Puścian M, Grabski W. Parallelization of Apriori algorithm using Charm++ library. InPhotonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015 2015 Sep 11 (Vol. 9662, pp. 1102-1109). SPIE.
  23. Rahm E, Do HH. Data cleaning: Problems and current approaches. IEEE Data Eng. Bull.. 2000 Dec;23(4):3-13.
  24. Rathee S, Kaul M, Kashyap A. R-Apriori: an efficient apriori based algorithm on spark. InProceedings of the 8th workshop on Ph. D. Workshop in information and knowledge management 2015 Oct 18 (pp. 27-34).
  25. Sakai H, Nakata M, Watada J. NIS-Apriori-based rule generation with three-way decisions and its application system in SQL. Information Sciences. 2020 Jan 1;507:755-71.
  26. Siddique MN, Hossain MA, Alam MS, Tokhi MO. Hidden markov model based fuzzy controller for flexible-link manipulator. InAdvances in Climbing and Walking Robots 2007 (pp. 642-651).
  27. Singh S, Garg R, Mishra PK. Observations on factors affecting performance of MapReduce based Apriori on Hadoop cluster. In2016 International Conference on Computing, Communication and Automation (ICCCA) 2016 Apr 29 (pp. 87-94). IEEE.
  28. Singh S, Garg R, Mishra PK. Observations on factors affecting performance of MapReduce based Apriori on Hadoop cluster. In2016 International Conference on Computing, Communication and Automation (ICCCA) 2016 Apr 29 (pp. 87-94). IEEE.
  29. Veeramalai S, Jaisankar N, Kannan A. Efficient web log mining using enhanced Apriori algorithm with hash tree and fuzzy. International journal of computer science & information Technology (IJCSIT) Vol. 2010 Aug;2:1-5.
  30. Wang Y, Li G, Xu Y, Hu J. An algorithm for mining of association rules for the information communication network alarms based on swarm intelligence. Mathematical Problems in Engineering. 2014;2014(1):894205.
  31. Yang J, Huang H, Jin X. Mining web access sequence with improved apriori algorithm. In2017 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC) 2017 Jul 21 (Vol. 1, pp. 780-784). IEEE.
  32. Yang Q, Fu Q, Wang C, Yang J. A matrix-based Apriori algorithm improvement. In2018 IEEE Third International Conference on Data Science in Cyberspace (DSC) 2018 Jun 18 (pp. 824-828).
  33. Yasmin RY, Saptawati P, Sitohang B. Classification based on constrained progressive Sequential Pattern mining: A proposed model. In2016 International Conference on Data and Software Engineering (IcoDSE) 2016 Oct 26 (pp. 1-5). IEEE.
  34. Yu H, Wen J, Wang H, Jun L. An improved Apriori algorithm based on the Boolean matrix and Hadoop. Procedia Engineering. 2011 Jan 1;15:1827-31.
  35. Zhang H, Chao X, Shi C. Closing the gap: A learning algorithm for lost-sales inventory systems with lead times. Management Science. 2020 May;66(5):1962-80.
  36. ZHANG Z, ZHANG L, ZHONG SC, GUAN J. Improving algorithm Apriori for data mining. InComputational Intelligence In Decision And Control 2008 (pp. 17-22).