[en] This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the evolutionary search as simple as possible in order to scale up for solving large instances. The experimental evaluation is performed considering a set of real problem instances, including a real-life problem of analyzing biomedical information in the Parkinson's disease map project. The main results demonstrate that the proposed evolutionary approaches are able to compute accurate trade-off solutions and efficiently handle the problem instance involving biomedical information.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Curi, María Eugenia
Carozzi, Lucía
Massobrio, Renzo
Nesmachnow, Sergio
DANOY, Grégoire ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
OSTASZEWSKI, Marek ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
BOUVRY, Pascal ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters
Date de publication/diffusion :
2018
Nom de la manifestation :
8th International Conference on Bioinspired Optimization Methods and Their Applications (BIOMA)
Date de la manifestation :
16-05-2018
Manifestation à portée :
International
Titre de l'ouvrage principal :
Bioinspired Optimization Methods and Their Applications
Maison d'édition :
Springer International Publishing, Cham, Inconnu/non spécifié
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