Mining clique frequent approximate subgraphs from multi-graph collections

Recently, some algorithms have been reported for mining multi-graph Frequent Approximate Subgraphs (FASs). This kind of algorithm has a large applicability in social network analysis, image classification tasks and clustering, among others. However, all of them mine a large number of patterns. For this reason, some algorithms for mining a subset of FASs have been proposed. In this paper, we propose an efficient algorithm for mining a subset of FASs on multi-graph collections. Our proposed algorithm becomes an alternative for reducing the number of mined FASs by computing the clique FASs. It is important to highlight that, to the best of our knowledge; our proposal is the first algorithm for mining clique FASs on multi-graph collections. Our proposal is compared against other reported solutions and evaluated over several synthetic and real-world multi-graphs. In addition, we show the usefulness of the patterns mined by our proposal for image classification.