An Improvised Fuzzy-C Means Clustering Based Optimization Using Map Reducing Model in Big Data

Abstract

Big data is gaining ground in many sectors, like industries, financial affairs, health etc. Since they can deal with huge volumes of data. Any real-world data may be appropriately organized by clustering the data using certain cluster methods, which in this clustering approach is a highly innovative & Improvised Fuzzy C-Means (IFCM) technique that can frame the data with excellent logic and very accurately. The MapReduce model is one of the most often and effectively utilized mining techniques for categorizing enormous amounts of data. In order to effectively process large amounts of data, this article combines the Stochastic Social Group Optimization (SSGO) with the MapReduce Model. The required outcomes were obtained by picking the best candidate solutions and arranging them into a reduction structure in order to acquire superior solutions. Ultimately, for each data sample, the recommended SSGO approach is used, which is based on categorizing with probable index values using succeeding possibility of data. For evaluation, the suggested technique is compared to three metrics: Sensitivity, Specificity, and Accuracy.