In recent years, graph representations have been used extensively for modelling complicated structural information, such as circuits, images, molecular structures, biological networks, weblogs, XML documents and so on. As a result, frequent subgraph mining has become an important subfield of graph mining. This paper presents a novel Frequent Pattern Graph Mining algorithm, FP-GraphMiner , that compactly represents a set of network graphs as a Frequent Pattern Graph (or FP-Graph ). This graph can be used to efficiently mine frequent subgraphs including maximal frequent subgraphs and maximum common subgraphs. The algorithm is space and time efficient requiring just one scan of the graph database for the construction of the FP-Graph , and the search space is significantly reduced by clustering the subgraphs based on their frequency of occurrence. A series of experiments performed on sparse, dense and complete graph data sets and a comparison with MARGIN , gSpan and FSMA using real time network data sets confirm the efficiency of the proposed FP-GraphMiner algorithm.
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