Paper Info
Title
A multiobjective variant of the Subdue graph mining algorithm based on the NSGA-II selection mechanism
Abstract
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.
Year
DOI
Venue
2010
10.1109/CEC.2010.5586400
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
Web sites,data mining,genetic algorithms,graph theory,search problems,NSGA-II selection mechanism,Pareto optimal set,Pareto-based multiobjective search strategy,Web sites,graph-based knowledge discovery algorithm,multiobjective subdue,nondominated sorting genetic algorithm,structural databases,subdue graph mining algorithm
Graph theory,Graph,Mathematical optimization,Computer science,Pareto optimal,Artificial intelligence,Knowledge extraction,Data mining algorithm,Genetic algorithm,Pareto principle,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-6909-3
7
0.46
References 
Authors
10
3
Name
Order
Citations
PageRank
Prakash Shelokar1383.33
Arnaud Quirin216813.68
Oscar Cordón31572100.75