Commodity-Grid Based Distributed Pattern Recognition Framework

Hudaya, A. and Khan, A.

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.

Cite as: Hudaya, A. and Khan, A. (2008). Commodity-Grid Based Distributed Pattern Recognition Framework. In Proc. Sixth Australasian Symposium on Grid Computing and e-Research (AusGrid 2008), Wollongong, NSW, Australia. CRPIT, 82. Kelly, W. and Roe, P., Eds. ACS. 27-34.

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