Abstract
A system based on the adaptation of the search principle used in ant colony optimization (ACO) for multiobjective graph-based data mining (GBDM) is introduced in this paper. Our multiobjective ACO algorithm is designed to retrieve the best substructures in a graph database by jointly considering two criteria, support and complexity. The experimental comparison performed with a classical GBDM method shows the good performance of the new proposal on three datasets. © 2010 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
Cordón, O., Quirin, A., & Romero-Zaliz, R. (2010). Multiple ant colony system for substructure discovery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6234 LNCS, pp. 472–479). https://doi.org/10.1007/978-3-642-15461-4_46
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