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- W2803791226 abstract "Grid computing offers significant platform of technologies where complete computational potential of resources could be harnessed in order to solve a complex problem. However, applying mining approach over distributed grid is still an open-end problem. After reviewing the existing system, it is found that existing approaches doesn’t emphasized on data diversity, data ambiguity, data dynamicity, etc. which leads to inapplicability of mining techniques on distributed data in grid. Hence, the proposed system introduces Optimized Clustering with Statistical Based local Model (OCSLM) in order to address this problem. A simple and yet cost effective machine-learning based optimization principle is presented which offers the capability to minimize the errors in mined data and finally leads to accumulation of superior quality of mined data. The study outcome was found to offer better sustainability with optimal computational performance when compared to existing clustering algorithms on distributed networking system." @default.
- W2803791226 created "2018-06-01" @default.
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- W2803791226 date "2018-05-17" @default.
- W2803791226 modified "2023-09-22" @default.
- W2803791226 title "OCSLM: Optimized Clustering with Statistical Based Local Model to Leverage Distributed Mining in Grid Architecture" @default.
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- W2803791226 doi "https://doi.org/10.1007/978-3-319-91192-2_32" @default.
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