A Comparative Study of Intelligent Agent Techniques for Distributed Data Databases

EasyChair Preprint no. 2099

Abstract

Intelligent agent-based systems have become a growing approach to decision-making in the business applications. Analyzing with the maximum knowledge enriches the rapid and accurate decisions in the competitive business environment. With the explosive development and growth of accessing information from the internet makes integration of multiple database data from diverse locations gives voluminous database and constructive effects in the decision making. Distributed data mining can accomplish a mining task with databases in diverse locations. Instead of moving of agents to all locations moving of agents to maximum density data locations is time consuming, space consuming and also increase of accuracy in decision making. This paper focuses on the comparative analysis of using intelligent-agent for distributed data mining, and density estimation based location selection for distributed data mining.

Keyphrases: density estimation, Distributed Data Mining, Intelligent–agents

@Booklet{EasyChair:2099,
  author = {M Dukitha and A Banumathi},
  title = {A Comparative Study of Intelligent Agent Techniques for Distributed Data Databases},
  howpublished = {EasyChair Preprint no. 2099},

  year = {EasyChair, 2019}}