Open AccessFirefly Algorithm based Map Reduce for Large-Scale Data Clustering
Author(s) -
Siva Krishna Reddy,
P. Sujatha,
Prasad Koti
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i8243.078919
Subject(s) - cluster analysis , computer science , data mining , cure data clustering algorithm , firefly algorithm , correlation clustering , scale (ratio) , canopy clustering algorithm , data stream clustering , euclidean distance , clustering high dimensional data , algorithm , machine learning , artificial intelligence , geography , cartography , particle swarm optimization
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