Dealing Big Data using Fuzzy C-Means (FCM) Clustering and Optimizing with Gravitational Search Algorithm (GSA)

Any data in the real world can be organized properly by Clustering the data using some clustering techniques and in these clustering techniques Fuzzy C-Means (FCM) is a very recent and better technique that can mold the data with good logic and in a highly accurate manner. FCM is same as K-Means clustering technique but FCM is developed with some Fuzzy means. FCM is associated with some constraints as FCM is more responsive as on the order how the clusters were arranged in the beginning of applying the technique. Even though it has its own defects when dealing with large data, it cannot get the finest solution as the data need some optimization? So to get some better accuracy, In our proposed paper we are applying an enhanced FCM for clustering and for obtaining precise results we are involving GSA based on gravitation laws and perception of masses. This optimizing technique (GSA) can be used to improve the limitations in order to gain a well-defined system performance; GSA can efficiently deal with many propositions of large data. Here we are going to develop a Map-Reduce mechanism to effectively deal with large data. The expected result can be achieved by choosing the better finest candidates and grouping them in the reduction mode to get the finest solution. Evaluation of practical results will be optimizing when compared to other existing similar techniques.