KM-MBFO: A Hybrid Hadoop Map Reduce Access for Clustering Big Data by Adopting Modified Bacterial Foraging Optimization Algorithm
Suja C Nair1, M. Sudheep Elayidom2, Sasi Gopalan3

1Suja C Nair, Research Scholar, Cusat, Cochin (Kerala), India.
2M. Sudheep Elayidom, Professor, School of Engineering, Cusat, Cochin (Kerala), India.
3Sasi Gopalan, Professor, School of Engineering, Cusat, Cochin (Kerala), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 146-152 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10240982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1024.0982S1119
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Abstract: K-Means Clustering is a very powerful and frequently used algorithm for the clustering, it has got its own limitation. The prevalent K-Means clustering algorithm used for grouping have inadequacies, for example, slow convergence rate, local optima trap, and so on. Therefore, many swarm knowledge based procedures combined with KM for clustering were presented and demonstrated their presentation, its variations and its applications in data grouping. In this paper we intend to propose a parallel organizing strategy for KM-MBFO mechanism that actualized in Hadoop Distributed File System (HDFS) for diminishing the execution time. This Mapper approach produces the populace for given data set for grouping. The Modified Bacterial Foraging Optimization (MBFO) algorithm finds the wellness of the populace to choose the optimal K values as far as execution time and classification error. Through simulated test results, we assess the demonstration of the proposed KM-BFO conspire.
Keywords: HDFS, Modified Bacterial Foraging Optimization (MBFO), K-Means Clustering, Big Data.
Scope of the Article: Clustering