Median graph computation for graph clustering
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In this paper, we are interested in the problem of graph clustering. We propose a new algorithm for computing the median of a set of graphs. The concept of median allows the extension of conventional algorithms such as the k-means to graph clustering, helping to bridge the gap between statistical and structural approaches to pattern recognition. Experimental results show the efficiency of the new median graph algorithm compared to the (only) existing algorithm in the literature. We also show its effective use in clustering a set of random graphs and in a content-based synthetic image retrieval system.