Efficient Algorithm for Mining Frequent Subgraphs (Static and Dynamic) based on gSpan

Abstract

sub graph mining is another active research topic in data mining. A graph is a general model to represent data and has been used in many domains like chemo informatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as sub graph testing, generally have higher time complexity than the corresponding operations on item sets, sequences, and trees. We investigated new approaches for frequent graph-based pattern mining in graph datasets and found that a novel algorithm called span (graph-based Substructure pattern mining), has been used as the standard for comparing performance of new algorithms. It is based on the pattern growth approach of frequent sub graph mining and hence discovers frequent substructures without candidate generation. span is based on a lexicographic order, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order, gSpan adopts the depth-first search strategy to mine frequent connected sub graphs efficiently. Based on the literature survey done, algorithms based on pattern growth approach and DFS strategy are found to be better in performance than algorithms based on Apriori approach and BFS strategy. In this paper we propose a new algorithm based on gSpan, for a special class of graphs characterised by the existence of unique node lablels.