Graph clustering is one of the key techniques to understand the structures present in the graph data. In addition to cluster detection, the identification of hubs and outliers is also a critical task as it plays an important role in the analysis of graph data. Recently, several graph clustering algorithms have been proposed and used in many application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on the structural clustering algorithm SCAN. Yet, SCAN algorithm has been designed for small graphs, without significant support to deal with big and dynamic graphs. In this paper, we propose DISCAN, a novel distributed and incremental graph clustering algorithm based on SCAN. We present an implementation of DISCAN on top of BLADYG framework, and experimentally show the efficiency of DISCAN in both large and dynamic networks.