293 Enhancing the Performance of Distributed Mining of Association Rules

Large-scale distributed systems, such as Grid computing environments, are recently regarded as promising platforms for data and computation-intensive applications like data mining. However, to improve the performance and achieve scalability by using these heterogeneous platforms, new data partitioning approaches and workload balancing features are needed. Association rule mining is one of the successful data mining techniques which is characterized by unpredictable load changes during the computation. In this paper we propose a dynamic load balancing strategy to enhance the performance of 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.