A new algorithm for graph mining

Mining frequent substructures has gained importance in the recent past. Number of algorithms has been presented for mining undirected graphs. Focus of this paper is on mining frequent substructures in directed labeled graphs since it has variety of applications in the area of biology, web mining etc. A novel approach of using equivalence class principle has been proposed for reducing the size of the graph database to be processed for finding frequent substructures. For generating candidate substructures a combination of L-R join operation, serial and mixed extensions have been carried out. This avoids missing of any candidate substructures and at the same time candidate substructures that have high probability of becoming frequent are generated.