executing association rule mining algorithms under a grid computing environment
Grids are now regarded as promising platforms for data and computation-intensive applications like data mining. However, the exploration of such large-scale computing resources necessitates the development of new distributed algorithms. The major challenge facing the developers of distributed data mining algorithms is how to adjust the load imbalance that occurs during execution. This load imbalance is due to the dynamic nature of data mining algorithms (i.e. we cannot predict the load before execution) and the heterogeneity of Grid computing systems. In this paper, we propose a dynamic load balancing strategy for distributed association rule mining algorithms under a Grid computing environment. We evaluate the performance of the proposed strategy by the use of Grid'5000. A Grid infrastructure distributed in nine sites around France, for research in large-scale parallel and distributed systems.
association rules grid computing dynamic load balancing apriori algorithm parallel association rule mining work migration