Agent Based Frameworks for Distributed Association Rule Mining: an Analysis
نویسندگان
چکیده
Distributed Association Rule Mining (DARM) is the task for generating the globally strong association rules from the global frequent itemsets in a distributed environment. The intelligent agent based model, to address scalable mining over large scale distributed data, is a popular approach to constructing Distributed Data Mining (DDM) systems and is characterized by a variety of agents coordinating and communicating with each other to perform the various tasks of the data mining process. This study performs the comparative analysis of the existing agent based frameworks for mining the association rules from the distributed data sources.
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