Dynamic load balancing of large-scale distributed association rule mining

The focus of this paper is to propose a dynamic load balancing strategy for parallel association rule mining algorithms in the context of a Grid computing environment. This strategy is built upon a distributed model which necessitates small overheads in the communication costs for load updates and for both data and work transfers. It also supports the heterogeneity of the system and it is fault tolerant.

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