Earticle

Clustering has an extensive and long history in a variety of scientific fields. Several recent studies of complex networks have suggested that the clustering analysis on networks has been an emerging research issue in data mining due to its variety of applications. Many graph clustering algorithms have been proposed in recent past, however, this clustering approach remains a challenging problem to solve real-world situation. In this work, we propose an aspiration criteria based graph clustering algorithm using stochastic local search for generating lower cost clustering results in terms of robustness and optimality for real-world complex network problems. In our proposed algorithm, all moves are meaningful and effective during the whole clustering process which indicates that moves are only accepted if the target node has neighbouring nodes in the destination cluster (moves to an empty cluster are the only exception to this instruction). An adaptive approach in our method is in incorporating the aspiration criteria for the best move (lower-cost changes) selection when the best non-tabu move involvements much higher cost compared to a tabued move then the tabued move is permitted otherwise the best non-tabu move is acceptable. Extensive experimentation with synthetic and real power-law distribution benchmark datasets show that our algorithm outperforms state-of-the-art graph clustering techniques on the basis of cost of clustering, cluster size, normalized mutual information (NMI) and modularity index of clustering results.