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OCSLM: Optimized Clustering with Statistical Based Local Model to Leverage Distributed Mining in Grid Architecture

  • M. Shahina Parveen
  • G. Narsimha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 765)

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.

Keywords

Grid computing Grid network Clustering Data mining Machine learning 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJNTUHyderabadIndia
  2. 2.Professor of CSE and HODJNTUH-CESSulthanpur, Sanga Reddy (D)India

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