Commodity-Grid Based Distributed Pattern Recognition Framework
نویسندگان
چکیده
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-theart 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-touse front-end for accessing a distributed system supporting complex operations.
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