OCSLM: Optimized Clustering with Statistical Based Local Model to Leverage Distributed Mining in Grid Architecture
| Autor: | M. Shahina Parveen, Gugulothu Narsimha |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Distributed mining
021103 operations research Grid network Computer science media_common.quotation_subject 020208 electrical & electronic engineering 0211 other engineering and technologies 02 engineering and technology Ambiguity computer.software_genre Grid Grid computing 0202 electrical engineering electronic engineering information engineering Leverage (statistics) Data mining Cluster analysis computer media_common Grid architecture |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319911915 CSOC (3) |
| DOI: | 10.1007/978-3-319-91192-2_32 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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