Medoid-based clustering using ant colony optimization

Menéndez, Héctor D. ORCID: https://orcid.org/0000-0002-6314-3725, Otero, Fernando E. B. and Camacho, David (2016) Medoid-based clustering using ant colony optimization. Swarm Intelligence, 10 (2) . pp. 123-145. ISSN 1935-3812 [Article] (doi:10.1007/s11721-016-0122-5)

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Abstract

The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets.

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