Firefly Algorithm based Map Reduce for Large-Scale Data Clustering
Siva Krishna Reddy1, Pothula Sujatha2, Prasad Koti3
1Siva Krishna Reddy, Department of Computer Science, Pondicherry University, Puducherry, India.
2Pothula Sujatha, Department of Computer Science, Pondicherry University, Puducherry, India.
3Prasad Koti, Department of Computer Science, Saradha Gangadharan College, Puducherry, India
Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1505-1511 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8243078919/19©BEIESP | DOI: 10.35940/ijitee.I8243.078919
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The technological advancement plays a major role in this era of digital world of growing data. Hence, there is a need to analyse the data so as to make good decisions. In the domain of data analytics, clustering is one of the significant tasks. The main difficulty in Map reduce is the clustering of massive amount of dataset. Within a computing cluster, Map Reduce associated with the algorithm such as parallel and distributed methods serve as a main programming model. In this work, Map Reduce-based Firefly algorithm known as MR-FF is projected for clustering the data. It is implemented using a MapReduce model within the Hadoop framework. It is used to enhance the task of clustering as a major role of reducing the sum of Euclidean distance among every instance of data and its belonging centroid of the cluster. The outcome of the experiment exhibits that the projected algorithm is better while dealing with gigantic data, and also outcome maintains the quality of clustering level.
Keywords: Hadoop, Map Reduce, Clustering, Firefly.
Scope of the Article: Clustering