A Parallel Computing Model of Agent enabled Mining of Globally Strong Association Rules

Gurpreet Singh Bhamra, Anil Kumar Verma, Ram Bahadur Patel


The intelligent agent based model is a popular approach to address scalable mining over large scale  distributed data and is characterized by variety of agents communicating and coordinating with each other to perform the various tasks of data mining (DM) process. Distributed Association Rule Mining (DARM) is the task of generating globally strong association rules from the global frequent itemsets in a distributed environment. In this study a parallel computing model of a multi agent system (MAS) called Agent enabled Mining of Globally Strong Association Rules (AeMGSAR) is presented based on the parallel itinerary of the mobile agents. A running environment is also designed for the implementation and performance study of AeMGSAR system.


Knowledge Discovery; Association Rules; Intelligent Agents; Multi-Agent System

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