An Efficient Mining of Dominant Entity Based Association Rules in Multi-databases

  • V. S. Ananthanarayana
Part of the Communications in Computer and Information Science book series (CCIS, volume 154)


Today, we have a large collection of data that is organized in the form of a set of relations which is partitioned into several databases. There could be implicit associations among various parts of this data. In this paper, we give a scheme for retrieving these associations using the notion of dominant entity. We propose a scheme for mining for dominant entity based association rules (DEBARs) which is not constrained to look for co-occurrence of values in tuples. We show the importance of such a mining activity by taking a practical example called personalized mining. We introduce a novel structure called multi-database domain link network (MDLN) which can be used to generate DEBARs between the values of attributes belonging to different databases. We show that MDLN structure is compact and this property of MDLN structure permit it to be used for mining vary large size databases. Experimental results reveal the efficiency of the proposed scheme.


Multi-databases Valueset Personalized mining Dominant entity Association rules 


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  1. 1.
    Shang, S.-j., Dong, X.-j., Li, J., Zhao, Y.-y.: Mining Positive and Negative Association Rules in Multidatabase Based on Minimum Interestingness. In: International Conference on Intelligent Computation Technology and Automation, pp. 791–794 (2008)Google Scholar
  2. 2.
    Li, H., Hu, X., Zhang, Y.: An Improved Database Classification Algorithm for Multi-database Mining. In: Deng, X., Hopcroft, J.E., Xue, J. (eds.) FAW 2009. LNCS, vol. 5598, pp. 346–357. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proc. 20th Int’l Conf. on VLDB, pp. 487–499 (1994)Google Scholar
  4. 4.
    Ananthanarayana, V.S., Subramanian, D.K., Narasimha Murty, M.: Scalable, distributed and dynamic mining of association rules. In: Prasanna, V.K., Vajapeyam, S., Valero, M. (eds.) HiPC 2000. LNCS, vol. 1970, pp. 559–566. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Ananthanarayana, V.S.: An Efficient Mining of Dominant Entity Based Association Rules in Multi-databases Using MDLN Structure, Technical Report, NITK (2011)Google Scholar
  6. 6.
    Zaki, M.J., Parthasarathy, S., Ogihara, M.: New algorithms for fast discovery of association rules. In: Int’l Conference on Knowledge Discovery and Data Mining, pp. 283–286 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • V. S. Ananthanarayana
    • 1
  1. 1.Department of Information TechnologyNational Institute of Technology KarnatakaSurathkalIndia

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