A Novel Data Partitioning Approach for Association Rule Mining on Grids

Abstract

Mining association rules refers to extracting useful knowledge from large databases. Algorithms of this technique are both data and computation-intensive, which make grid platforms very attractive for them. However, to exploit these platforms, new data partitioning features are required where the specificities of both association rule mining technique and grids must be taken into consideration. In this paper, we propose a novel data partitioning approach for distributed association rule mining algorithms in the context of a grid computing environment. We conduct experiments using the French research grid ”Grid’5000”. Experimental results confirm that our data partitioning approach is very sufficient for balancing the load when homogeneous clusters are used. For heterogeneous clusters, the proposed data partitioning approach constitute the preprocessing phase of the process of dynamic load balancing of distributed association rule mining.