Data-mining model based on multi-agent for the intelligent distributed framework

  • Authors:

  • Romeo Mark A. Mateo;Insook Yoon;Jaewan Lee

  • Affiliations:

  • School of Electronic and Information Engineering, Kunsan National University, Kunsan, Chonbuk, South Korea;School of Electronic and Information Engineering, Kunsan National University, Kunsan, Chonbuk, South Korea;School of Electronic and Information Engineering, Kunsan National University, Kunsan, Chonbuk, South Korea

  • Venue:
  • KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
  • Year:
  • 2008

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Abstract

Recent researches in distributed system include intelligent resource finding, dynamic replication and adaptive load balancing schemes which focus on improving specific technique. In this paper, an intelligent distributed framework is presented to address the use of intelligent models for adaptive distributed object groups. Moreover, this paper proposes the agent-based data-mining model for implementing adaptive schemes using data mining algorithms and efficient interactions using multi-agent system. The k-means algorithm constructs group classes of object, multilayer perceptron classifies the client requests using the classes constructed from k-means and patterns generated from Apriori algorithm determine the next object needed to be replicated. For efficient interactions, the data mining is modeled in multi-agent system. Simulation result using the proposed model shows great improvements on serving clients by minimizing delay time and optimizes system performance by efficient load distribution.