Abstract
This article presents \(MO-Mine_{clust}\) a first package of the platform in development \(MO-Mine\). This platform aims at providing optimization algorithms, and in particular multi-objective approaches, to deal with classical datamining tasks (Classification, association rules...). This package \(MO-Mine_{clust}\) is dedicated to clustering. Indeed, it is well-known that clustering may be seen as a multi-objective optimization problem as the goal is both to minimize distances between data belonging to a same cluster, while maximizing distances between data belonging to different clusters. In this paper we present the framework as well as experimental results, to attest the benefit of using multi-objective approaches for clustering.
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This work has been realized with the support of the french project ANR-13-TECS-0009.
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Fisset, B., Dhaenens, C., Jourdan, L. (2015). \(MO-Mine_{clust}\): A Framework for Multi-objective Clustering. In: Dhaenens, C., Jourdan, L., Marmion, ME. (eds) Learning and Intelligent Optimization. LION 2015. Lecture Notes in Computer Science(), vol 8994. Springer, Cham. https://doi.org/10.1007/978-3-319-19084-6_30
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DOI: https://doi.org/10.1007/978-3-319-19084-6_30
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