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

Author

Shelokar, Prakash ; Quirin, Arnaud ; Cordón, Óscar

Author_Institution

Eur. Centre for Soft Comput., Mieres, Spain

fYear

2010

fDate

18-23 July 2010

Firstpage

1

Lastpage

8

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.

Keywords

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; Complexity theory; Data mining; Databases; Measurement; Optimization; Search problems; Web sites;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2010 IEEE Congress on

Conference_Location

Barcelona

Print_ISBN

978-1-4244-6909-3

Type

conf

DOI

10.1109/CEC.2010.5586400

Filename

5586400