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

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.