A Novel Data Partitioning Approach for Association Rule Mining on Grids
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, the authors propose a novel data partitioning approach for distributed association rule mining algorithms in the context of a grid computing environment. They conduct experiments using the French research grid "Grid'5000". Experimental results confirm that their data partitioning approach is very sufficient for balancing the load when homogeneous clusters are used.