Optimal Fuzzy C-means Clustering Technique for Big Data Analytics with Map Reduce based on Hybrid Optimization Algorithm

Dandugala Lakshmi Srinivasulu and K. Suvarna Vani

The Map Reduce programming model has parallel processing capabilities for analyzing a large network, and Map Reduce is a programming model that allows for easy creation of parallel applications that can process large data in large clusters of object machines. 7Map Reduce or its open-source similar Hadoop is a powerful tool for building such applications. We develop a Map reduce programming model for the data reduction process in Big data. Map reduce programming model is generally used to create more parallel applications that process large amounts of data. an improved fuzzy c-means clustering algorithm with advanced data resolution is used for clustering a large data set. In our proposed research we introduce a cuckoo search-based Firefly algorithm. Here we have clusters of data with the help of fuzzy C-means clustering algorithm. The evaluation of the proposed technique is investigated in terms of the total cost, time and memory utilization. The implementation will be done using cloud-sim with JAVA in map reduce framework.

Volume 11 | 10-Special Issue

Pages: 1298-1310

DOI: 10.5373/JARDCS/V11SP10/20192975