Pixel clustering by using complex network community detection technique

Traditional data clustering techniques present difficulty in determination of clusters of arbitrary forms. On the other hand, graph theoretic methods seek topological orders among input data and, consequently, can solve the above mentioned problem. In this paper we present an improved graph theoretic model for data clustering. The clustering process of this model is composed of two steps: network formation by using input data and hierarchical network partition to obtain clusters in different scales. Our network formation method always produces a connected graph with densely linked nodes within a community and sparsely linked nodes among different communities. The community detection technique used here has the advantage that it is completely free from physical distances among input data. Consequently, it is able to discover clusters of various forms correctly. Computer simulations show the promising performance of the model.