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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(More)
Extracting useful knowledge from data sets measuring in gigabytes and even terabytes is a challenging research area for the data mining community. Sequential approaches suffer from a performance problem due to the fact that they have to mine voluminous databases. Parallelism is introduced as an important solution that could improve the response time and the(More)
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(More)
Association rule mining is one of the most important data mining techniques. Algorithms of this technique search a large space, considering numerous different alternatives and scanning the data repeatedly. Parallelism seems to be the natural solution in order to be able to work with industrial-sized databases. Large-scale computing systems, such as Grid(More)
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