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Intelligent Computing and Information and Communication pp 77–85Cite as

Mobile Agent-Based Frequent Pattern Mining for Distributed Databases

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 673))

Abstract

In today’s world of globalization, business organizations produce information from many branch offices of their business while operating across the globe and hence lead to large chunk of distributed databases. There is an innate need to look at this distributed information that leverages the past, monitors the present, and predicts the future with accuracy. Mining large distributed databases using client–server model is time-consuming and sometimes impractical because it requires huge databases to be transferred over very long distances. Mobile agent technology is a promising alternative that addresses the issues of client–server computing model. In this paper, we have proposed an algorithm called MADFPM for frequent pattern mining of distributed databases that use mobile agents. We have shown that the performance of MADFPM is better compared to the conventional client–server approach.

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References

  1. Agrawal R., Imielinski, T., and Swami, A. (1993), “Mining association rules between sets of items in large databases”. In Proc. of ACM-SIGMOD, (SIGMOD’93), pp. 207–216.

    Google Scholar 

  2. Paul S. Bradley, J. E. Gehrke, Raghu Ramakrishnan and Ramakrishnan Srikant (2002), ‘Philosophies and Advances in Scaling Mining Algorithms to Large Databases”. Communications of the ACM.

    Google Scholar 

  3. Chattratichat, J., Darlington, J, et al. (1999), “An Architecture for Distributed Enterprise Data Mining”, 7th Intl. Conf. on High Performance Computing and Networking.

    Google Scholar 

  4. You-Lin Ruan, Gan Liu, Quin-Hua Li (2005), “Parallel Algorithm for Mining Frequent Items”, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, pp-18–21.

    Google Scholar 

  5. U.P. Kulkarni, P.D. Desai, Tanveer Ahmed, J.V. Vadavi, A.R. Yardi (2007), “Mobile Agent Based Distributed Data Mining”. International Conference on Computational Intelligence and Multimedia Applications, pp. 18–24.

    Google Scholar 

  6. Saleem Raja, George Dharma Prakash Raj, (2013), “Mobile Agent based Distributed Association Rule Mining”, International Conference on Computer Communication and Informatics (ICCCI), 2013.

    Google Scholar 

  7. LIU Xiang (2008), “An Agent-based Architecture for Supply Chain Finance Cooperative Context-aware Distributed Data Mining Systems”. 3rd International Conference on Internet and Web Applications and Services.

    Google Scholar 

  8. Ogunda A.O., Folorunso O., Ogunleye G.O., (2011), “Improved cost models for agent-based association rule mining in distributed databases, Anale. SeriaInformatică. Vol. IX fasc. 1 – 2011.

    Google Scholar 

  9. J. Han, J. Pei, Y. Yin and R. Mao (2004), “Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach”. Data Mining and Knowledge Discovery, 8(1), pp. 53–87.

    Google Scholar 

  10. Keshavamurthy B.N., Mitesh Sharma and DurgaToshniwal (2010), “Efficient Support Coupled Frequent Pattern Mining Over Progressive Databases”, International Journal of Database Systems, Vol.-2, No-2, pp-73–82.

    Google Scholar 

  11. Mengling Feng, Jinyan Li, Guozhu Dong, Limsoon Wong (2009), “Maintenance of Frequent Patterns: A Survey”, published in IGI Global, XIV Chapter, pp-275–295.

    Google Scholar 

  12. Syed K. Tanbeer, C. F. Ahmed, B-S Jeong (2009), “Parallel and Distributed Algorithms for Frequent PatternMining in Large Databases”. IETE Technical Review, Vol. 26, Issue 1, pp-55–66.

    Google Scholar 

  13. Raquel Trillo, Sergio Ilarri, Eduardo Mena (2007), “Comparison and Performance Evaluation of Mobile Agent Platforms”, Third International Conference on Autonomic and Autonomous Systems (ICAS’07), pp. 41.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, BVBCET, Hubballi, India

    Yashaswini Joshi & Shashikumar G. Totad

  2. Department of Information Science, KLEIT, Hubballi, India

    R. B. Geeta

  3. Department of CS & SE, Andhra University, Visakhapatnam, India

    P. V. G. D. Prasad Reddy

Authors
  1. Yashaswini Joshi
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  2. Shashikumar G. Totad
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  3. R. B. Geeta
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  4. P. V. G. D. Prasad Reddy
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Corresponding author

Correspondence to Yashaswini Joshi .

Editor information

Editors and Affiliations

  1. Department of Computer Software, University of Aizu, Aizuwakamatsu, Fukushima, Japan

    Subhash Bhalla

  2. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Department of Information Technology, MIT College of Engineering, Pune, Maharashtra, India

    Anjali A. Chandavale

  4. Department of Information Technology, MIT College of Engineering, Pune, Maharashtra, India

    Anil S. Hiwale

  5. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

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Cite this paper

Joshi, Y., Totad, S.G., Geeta, R.B., Prasad Reddy, P.V.G.D. (2018). Mobile Agent-Based Frequent Pattern Mining for Distributed Databases. In: Bhalla, S., Bhateja, V., Chandavale, A., Hiwale, A., Satapathy, S. (eds) Intelligent Computing and Information and Communication. Advances in Intelligent Systems and Computing, vol 673. Springer, Singapore. https://doi.org/10.1007/978-981-10-7245-1_9

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  • DOI: https://doi.org/10.1007/978-981-10-7245-1_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7244-4

  • Online ISBN: 978-981-10-7245-1

  • eBook Packages: EngineeringEngineering (R0)

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