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Multiple Ant Colony System for Substructure Discovery

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6234))

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.

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Cordón, O., Quirin, A., Romero-Zaliz, R. (2010). Multiple Ant Colony System for Substructure Discovery. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2010. Lecture Notes in Computer Science, vol 6234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15461-4_46

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  • DOI: https://doi.org/10.1007/978-3-642-15461-4_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15460-7

  • Online ISBN: 978-3-642-15461-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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