Advertisement

An Enhanced Knowledge Integration of Association Rules in the Privacy Preserved Distributed Environment to Identify the Exact Interesting Pattern

  • Sujni PaulEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 29)

Abstract

Numerous research works are carried out in the field of data mining, especially in the areas of association rule mining, knowledge integration in the distributed data mining and privacy intense data mining. In the distributed data mining environment, the local data mining systems distributed across the environment. The way these local mining systems distributed in the environment, plays a major role in the process of knowledge integration. If all the local data mining systems are deployed in an organization, there will not be any impact. If the local data mining systems distributed across multiple organizations, that would cause a major impact in the process of knowledge integration. The problems are caused due to the privacy related issues and the agreement between those organizations. Though there are existing generic approaches to integrate the knowledge in the distributed mining, focus of this paper is to propose an enhanced algorithm specific to integration of association rules in the privacy protected distributed data mining environment and to find the interesting rules which are sub sets of an actual rule.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Databases (VLDB 1994, Santiago de Chile), pp. 487–499. Morgan Kaufmann, San Mateo (1994)Google Scholar
  3. 3.
    Margahny, M.H., Mitwaly, A.A.: Fast algorithms for mining association rules. In: AIML 05 Conference, CICC, Cairo, Egypt, 19–21 December 2005 (2005)Google Scholar
  4. 4.
    An implementation of the FP-growth Algorithm Christian Borgelt Workshop Open Source Data Mining Software (OSDM 2005, Chicago, IL), pp. 1–5. ACM Press, New York (2005)Google Scholar
  5. 5.
    Agrawal, R., Shafer, J.C.: Parallel mining of association rules. Distrib. Syst. Online (2004)Google Scholar
  6. 6.
    Paul, S., Saravanan, V.: Knowledge integration in a parallel and distributed environment with association rule mining using XML data. IJCSNS Int. J. Comput. Sci. Netw. Secur. 8(5) (2008)Google Scholar
  7. 7.
    Zhang, C., Liu, M., Nie, W., Zhang, S.: Identifying global exceptional patterns in multidatabase mining. IEEE Comput. Intell. (2010)Google Scholar
  8. 8.
    Adhikari, A., Ramachandrarao, P., Prasad, B., Adhikari, J.: Mining multiple large data sources. Int. Arab J. Inf. Technol. 7(3) (2010)Google Scholar
  9. 9.
    Wu, X., Zhang, S.: Synthesizing high-frequency rules from different data sources. IEEE Trans. Knowl. Data Eng. 15(2), 353–367 (2003)CrossRefGoogle Scholar
  10. 10.
    Nedunchezhian, R., Anbumani, K.: Post mining - discovering valid rules from different sized data sources. World Acad. Sci. Eng. Technol. 7 (2007)Google Scholar
  11. 11.
    Ramkumar, T., Srinivasan, R.: Multi-level synthesis of frequent rules from different data-sources. Int. J. Comput. Theory Eng. 2(2) (2010)Google Scholar
  12. 12.
    Ramkumar, T., Srinivasan, R.: Modified algorithm for synthesizing high frequency rules from different data sources. Knowl. Inf. Syst. 17(3), 313–334 (2008)CrossRefGoogle Scholar
  13. 13.
    Panchal, M.C., Scholar, P.G.: Privacy preserving of association rule mining: a review. Int. J. Innov. Adv. Comput. Sci. IJIACS 4(Special Issue), September 2015. ISSN 2347 – 8616Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Information ScienceDubai Men’s College, Higher Colleges of TechnologyDubaiUnited Arab Emirates

Personalised recommendations