A multiobjective variant of the Subdue graph mining algorithm based on the NSGA-II selection mechanism

In this work we propose a Pareto-based multi-objective search strategy for subgraph mining in structural databases. The method is an extension of Subdue, a classical graph-based knowledge discovery algorithm, and it is thus called MultiObjective Subdue (MOSubdue). MOSubdue incorporates the NSGA-II´s crowding selection mechanism in order to retrieve a well distributed Pareto optimal set of meaningful subgraphs showing different optimal trade-offs between support and complexity, in a single run. The good performance of the proposed approach is empirically demonstrated by using a reallife data set concerning the analysis of web sites.