Clustering and Analysis of Dynamic Ad Hoc Network Nodes Movement Based on FCM Algorithm

Clustering is a major exploratory data mining activity, and a popular statistical data analysis technique used in many fields. Cluster analysis generally speaking isn't just an automated function, but rather reiterated information exploration procedure or multipurpose dynamic optimisation comprising trial and error. Parameters for pre-processing and modeling data frequently need to be modified until the output hits the desired properties. -Data points in fuzzy clustering may probably belong to several clusters. Each data point is assigned membership grades. Such grades of membership reflect the degree to which data points belong to each cluster. The Fuzzy C-means clustering (FCM) algorithm is among the most widely used fuzzy clustering algorithms. In this paper we use this method to find typological analysis for dynamic Ad Hoc network nodes movement and demonstrate that we can achieve good performance of fuzziness on a simulated data set of dynamic ad hoc network nodes (DANET) and how to use this principle to formulate node clustering as a partitioning problem. Cluster analysis aims at grouping a collection of nodes into clusters in such a way that nodes seeing a high degree of correlation within the same cluster, whereas nodes members of various clusters are extremely dissimilar in nature. The FCM algorithm is used for implementation and evaluation the simulated data set using NS2 simulator with optimized AODV protocol. The results from the algorithm 's application show the technique achieved the maximum values of stability for both cluster centers and nodes (98.41 %, 99.99 %) respectively and has the highest accuracy (stability) compared to previous methods.