Data mining: concepts and techniques
Data mining: concepts and techniques
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Strategies for Dynamic Load Balancing on Highly Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Top Down FP-Growth for Association Rule Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Grid'5000: A Large Scale and Highly Reconfigurable Grid Experimental Testbed
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Design and implementation of a data mining grid-aware architecture
Future Generation Computer Systems - Special section: Data mining in grid computing environments
New challenges in dynamic load balancing
Applied Numerical Mathematics - Adaptive methods for partial differential equations and large-scale computation
A survey of load balancing in grid computing
CIS'04 Proceedings of the First international conference on Computational and Information Science
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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 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.