Commodity-grid based distributed pattern recognition framework

Authors:
Anang Hudaya Muhamad Amin;Asad I. Khan
Affiliations:
Monash University, Clayton, Melbourne, Victoria;Monash University, Clayton, Melbourne, Victoria
Venue:
AusGrid '08 Proceedings of the sixth Australasian workshop on Grid computing and e-research - Volume 82
Year:
2008

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Abstract

Large-scale pattern recognition for data mining requires significant processing resources. Distributed pattern recognition provides an avenue for achieving large-scale pattern recognition by using a state-of-the-art data classifier for fast tracking large-scale data analyses. In this paper, we will introduce a framework for distributed pattern recognition which is grid enabled and employs a distributed single-cycle learning Associative Memory approach. The framework comprises commodity-grid network for pattern recognition processing using the single-cycle approach. Our research has shown that the distributed pattern recognition using this framework will provide a fast and reliable resource for use in data mining. Our work also shows that the commodity-grid provide an easy-to-use front-end for accessing a distributed system supporting complex operations.