Metagraph-Based Substructure Pattern Mining

Abstract

The need for mining structured data has increased in the past few years. One of the best studied data structures in computer science and discrete mathematics are graphs. Graph based data mining has become quite popular in the last few years. In this paper author presented Metagraph based data mining as a new approach in the field of traditional graph based mining. Metagraph is a new graph theoretic construct having set-to-set mapping in place of node to node as in conventional graph structure. We investigate new approaches for frequent Metagraph-based pattern mining in Metagraph datasets. We propose an algorithm for metagraph graph-based Substructure pattern mining which discovers frequent substructures without candidate generation. We apply a new lexicographic order for metagraphs, and map each sub metagraph to a unique minimum DFS code as its canonical label. Based on this lexicographic order. We develop an algorithm which adapts the depth-first search strategy to mine frequent connected submetagraph efficiently.