Clustering of Huge Data with Fuzzy C-Means and applying Gravitational Search Algorithm for Optimization
R Venkat1, K Satyanarayan Reddy2
1R Venkat, Assistant Professor , DVR & Dr HS MIC College of Technology, Kanchikacherla, Dist. Krishna.
2K Satyanarayan Reddy, Head of the Department, Information Science & Engineering, Cambridge Institute of Technology, Bangalore, Karnataka State, India.

Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3206-3209 | Volume-8 Issue-5, January 2020. | Retrieval Number: D9130118419/2020©BEIESP | DOI: 10.35940/ijrte.D9130.018520

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Abstract: 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.
Keywords: Fuzzy C-Means Clustering, Gravitational Search Algorithm, Huge Data, K-Means and Preprocessing.
Scope of the Article: Parallel and Distributed Algorithms.