International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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A New Model to Enhance Efficiency in Distributed Data Mining Using Mobile Agent

  • Received : 2021.03.05
  • Published : 2021.03.30
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Abstract

As a result of the vast amount of data that is geographically found in different locations. Distributed data mining (DDM) has taken a center stage in data mining. The use of mobile agents to enhance efficiency in DDM has gained the attention of industries, commerce and academia because it offers serious suggestions on how to solve inherent problems associated with DDM. In this paper, a novel DDM model has been proposed by using a mobile agent to enhance efficiency. The main idea behind the model is to use the Naive Bayes algorithm to give the mobile agent the ability to learn, compare, get and store the results on it from each server which has different datasets and we found that the accuracy increased roughly by 0.9% which is our main target.

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References

  1. J. P. J. &. K. M. Han, Data mining: concepts and techniques, Elsevier, 2011.
  2. M. H. Dunham, Data mining: Introductory and advanced topics, India: Pearson Education India, 2006.
  3. R. J. Roiger, Data Mining: A Tutorial-Based Primer, Second Edition, 2017.
  4. A. Algarni, "Data mining in education," International Journal of Advanced Computer and Application, vol. 7, no. 6, pp. 456-461, 2016.
  5. C. C. Aggarwal, Data mining: the textbook, Springer, 2015.
  6. P. N. S. M. &. K. V. Tan, Introduction to data mining, Pearson Education India, 2016.
  7. N. &. H. W. Jothi, Data mining in healthcare-a review, vol. 72, Procedia computer science, 2015, pp. 306-301. https://doi.org/10.1016/j.procs.2015.12.145
  8. N. M. A. M. &. M. H. Alotaibi, "Agent-based big Data mining," International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 1.1, 2019.
  9. M. J. Zaki, Parallel and distributed data mining: An introduction. In large-scale parallel data mining, berlin, heidelberg: Springer, 2000, pp. 1-23.
  10. Y. Fu, "Distributed data mining: An overview," Newsletter of the IEEE Technical Committee on Distributed Processing, vol. 4, no. 3, pp. 5-9, 2001.
  11. L. L. L. D. L. L. K. S. Z. W. M. .. &. L. P. Zeng, "Distributed data mining: a survey," in Information Technology and Management, vol. 13, 2012, pp. 403-409. https://doi.org/10.1007/s10799-012-0124-y
  12. S. G. Devi, "A survey on distributed data mining and its trends," International Journal of Research in Engineering & Technology, vol. 2, no. 3, pp. 107-120, 2014.
  13. U. P. T. K. K. M. S. R. &. Y. A. R. Kulkarni, "Exploring the capabilities of mobile agents in distributed data mining," International Database Engineering and Applications Symposium, pp. 277-280, 2006.
  14. W. L. J. C. W. C. H. C. &. z. J. Gan, "Data mining in distributes enviromnet," in Data Mining and Knowledge Discovery, vol. 7(6), Springer, 2017.
  15. A. A. V. P. K. S. Z. P. &. U. A. Ali, "Distributed data mining systems: techniques, approaches and algorithms," in MATEC Web of Conferences. EDP Sciences, 2018.
  16. M. R. Chikhale, "Study of Distributed Data Mining Algorithm anf Trends," ISOR Journal of Computer Engineering, pp. 2278-0661, 2016.
  17. S. &. N. M. Urmela, "Approaches and Techniques of Distributed Data Mining," International Journal of Engineering and Technology, vol. 9(1), p. 69, 2017.
  18. C. Sahai M. J. & Morin, "Mobile agent for enabling mobile user aware application," in Autonomous agents, 1998.
  19. A. N. &. R. R. Moghadam, "Multi agent distributed mining approach for classifying meteorology data," International journal of environmental science and technology, vol. 15(1), pp. 149-158, 2018. https://doi.org/10.1007/s13762-017-1351-x
  20. S. Aswanandini, "Survey of security in Multi-Agent System for Distributed Data Mining," International Journal of Innovative Computer cience & Engineering, vol. 4(3), 2017.
  21. A. H. Nure, "Distributed data mining using multi-agent data," International Research Journal of Engineering and Technology, 2017.
  22. V. &. N. P. Devasekhar, Multi-Agent Distributed Data Mining Challenges And Research Directions, 2020.
  23. S. Bosse, "Industrial Agents and Distributed Agent-based Learning," in Multidisciplinary Digital Publishing institute Proceedings, 2016.
  24. R. &. M. L. J. Hemamalini, "n analysis on multi-agent based distributed data mining system," International Journal of Scientific and Research Publications, vol. 4, no. 6, pp. 1-6, 2014.
  25. Q. R. N. S. B. J. I. S. S. V. V. K. Q. H. &. C. K. Wu, "On computing mobile agent routes for data fusion in distributed sensor networks," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 6, pp. 740-753, 2004. https://doi.org/10.1109/TKDE.2004.12
  26. D. M. &. M. N. Khan, "The adaptability of conventional data mining algorithms through intelligent mobile agents in modern distributed systems," International Journal of Computer Science Issues, vol. 9, no. 1, p. 38, 2012.
  27. S. &. E. U. Bosse, "Augmented virtual reality," in In Multidisciplinary Digital Publishing Institute Proceedings, 2018.
  28. Y. t. S. G. G. R. B. &. r. P. P. Joshi, "Mobile Agent-Based Frequent Pattern Mining for Distributed Database," in Intelligent Computing and Information and Communication, Singapore, Springer, 2018, pp. 77-85.
  29. M. &. B. A. Dukitha, A Comparative Study of Intelligent Agent Techniques for Distributed Data Databases, EasyChair, 2019.
  30. A. &. R. E. Raja, "MADPARM: Mobile Agent based Distributed and Parallel Association Rule Mining," International Journal of Engineering Trends and Technology, vol. 49(6), 2017.
  31. M. &. B. S. Ali, "Probabilistic normed load monitoring in large scale distributed system using mobile agent," in Future Generation Computer Systems, 2019, pp. 148-167.
  32. O. &. I. S. Urra, "Spatial crowdsourcing with mobile agents in vehicular network," in Vehicular Communications, vol. 17, 2019, pp. 10-34. https://doi.org/10.1016/j.vehcom.2019.03.004
  33. S. Das, "Graph Explorations with Mobile Agents," in Distributed Computing by Mobile Entities, Cham, Springer, 2019, pp. 403422.
  34. I. Satoh, Mobile agents. In Handbook of Ambient Intelligence and Smart Environments, Boston, MA: Springer, 2010, pp. 771791.
  35. S. Das, Mobile agents in distributed computing: Network exploration, vol. 1, bulletin of EATCS, 2013, p. 109.
  36. D. B. Lange, "Mobile objects and mobile agents: The future of distributed computing?," in In European conference on objectoriented programming, Berlin, Heidelberg, 1998.
  37. M. L. &. P. G. Griss, Accelerating development with agent components, vol. 34, Computer, 2001, pp. 37-43. https://doi.org/10.1109/2.920610
  38. A. McCallum, "Bayesian Network Represention," in Graphical Models, 2019.
  39. N. &. T. G. Sneha, "Analysis of diabetes mellitus for early prediction using optimal features selection," in Journal of Big Data, 2019.
  40. K. N. &. S. N., "A Naive Bayesian Classifier for Educational Qualification," Indian Journal of Science and Technology, vol. 8(16), 2015.