DATMin-A Novel large Data mining Technique for Concept Evolution and Feature Evolution using Map Reduce Paradigm
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 11
Abstract
The Distributed Large Scale data mining using map reduce paradigm is been carried out in large scale using classification and clustering algorithm as it is treated as computational intensive task. The Data Classification and Clustering algorithm using supervised and unsupervised learning models has imposed much impact in map reduce paradigm due to evolution in Concept and feature in the large streaming application. Hence it becomes mandatory to model a system which is capable of handling of the data evolution in the large data. In this Paper, we propose a novel technique named as “DATMin” which develop a learning model in the map reduce paradigm using key value pair processing & Concept based Mining techniques for map and reduce functions. The proposed Technique is capable of handling the diverse computation characteristics such as accuracy and complexity. It will be handled using sophisticated iterative models for feature extraction, classification and feature or concept evolution determination. The Experimental results prove that proposed System outperforms other state of approaches using evaluation metrics such Runtime &Mean Error and reduces I/O overhead to much extent.
Authors and Affiliations
Ms. K. Amrita Priya, Mrs. D. Priyadarshini
Keywords
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- EP ID EP22777
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