Multi-agent based data mining aggregation approaches using machine learning techniques

  • Authors

    • V Devasekhar GNITC
    • P Natarajan VITU
    2018-06-23
    https://doi.org/10.14419/ijet.v7i3.9631
  • Data Aggregation, Multi Agents, Machine Learning, Aggregation.

  • Abstract

    Data Mining is an extraction of important knowledge from the various databases using different kinds of approaches. In the multi agent, distributed mining the knowledge aggregation is one of challenging task. This paper tries to optimize the problem of aggregation and boils down into the solution, which is derived based on the machine learning statistical features of each agents. However, in this paper a novel optimization algorithm called Multi-Agent Based Data Mining Aggregation (MABDA) is used for present day’s scenarios. The MBADA algorithm has agents which collect extracted knowledge and summarizes the various levels of agent’s cluster data into an aggregation with maximum accuracies. To prove the effectiveness of the proposed algorithm, the experimental results are compared with relatively existing methods.

     

  • References

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  • How to Cite

    Devasekhar, V., & Natarajan, P. (2018). Multi-agent based data mining aggregation approaches using machine learning techniques. International Journal of Engineering & Technology, 7(3), 1136-1139. https://doi.org/10.14419/ijet.v7i3.9631

    Received date: 2018-02-20

    Accepted date: 2018-05-30

    Published date: 2018-06-23