Clustering for Big Networks using Probabilistic Tabu Search

Fuzzy C-means (FCM) is a clustering method used for collecting similar data elements within the group according to specific measurements. Tabu is a heuristic algorithm. In this paper, Probabilistic Tabu Search for FCM implemented to find a global clustering based on the minimum value of the Fuzzy objective function. We present Tabu Search for Fuzzy c-Means to estimate the best clustering by finding the best values of Fuzzy objective function and apply it on different types of real networks, the results show the ability of Tabu search to find the global solution and determine the centroids, this step is important to find the community detection of the big networks.