Open AccessClustering of Huge Data with Fuzzy C-Means and applying Gravitational Search Algorithm for Optimization
Author(s) -
Ramanan Sridaran Venkat,
K. Satyanarayan Reddy
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d9130.018520
Subject(s) - cluster analysis , fuzzy clustering , preprocessor , data mining , computer science , fuzzy logic , outcome (game theory) , data pre processing , algorithm , artificial intelligence , mathematics , mathematical economics
There is a lot of bulk data which can be efficiently structured using some Clustering mechanism, among these mechanisms Fuzzy C-Means (FCM) Clustering technique is very new and can handle this bulk data logically and in a well precise mode. FCM is a better technique when compared to K-Means as FCM is designed with Fuzzy Concerns. But clustering only cannot give precise outcome, that’s the reason we are involving an Optimization technique for tuning the results and Gravitational Search Algorithm (GSA) Optimization can makes the outcome more precise. GSA is concerned with gravity principles. GSA tailors the defects and transitions into a well structure system and finally FCM will be optimized using GSA. This System is developed with Map-Reduced method. Here in this paper, a discussion is being presented with different existing techniques that were previously used to structure the data and it is discussed how FCM with GSA is better technique when compared to those techniques and some sample Preprocessing Patterns and k-means clustering results are obtained as a first step of research.