Mining approximate frequent subgraph with sampling techniques

Graph Mining has gained importance due to its wide range of applications. As graph mining is a form of data mining where data is depicted in the form of graphs. The data when represented in the form of graphs helps to model the real scenario and relationship well. Discovery of interesting pattern from real relationship is of utmost beneficial. Frequent Subgraph Mining (FSM) is a significant area of graph mining. We accompany our approach in the field of FSM by adding a graph sampling function. This sampling function takes the single graph and a sampling percentage as input and produces a sampled graph which is similar in properties with original graph. This sampled graph is then taken as input while implementing the GraMi framework. Our sampling approach successfully mines the subgraphs with lower frequency threshold thus significantly improving the efficiency of the original GraMi framework.