A multiobjective variant of the Subdue graph mining algorithm based on the NSGA-II selection mechanism
作者： Prakash Shelokar , Arnaud Quirin , Oscar Cordón , None
关键词:
摘要: 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, classical graph-based knowledge discovery algorithm, and it thus called MultiObjective Subdue (MOSubdue). MOSubdue incorporates the NSGA-II's crowding selection mechanism order to retrieve well distributed Pareto optimal set meaningful subgraphs showing different trade-offs between support complexity, single run. good performance proposed approach empirically demonstrated by using reallife data concerning analysis web sites.
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