Medoid-based clustering using ant colony optimization

Menéndez, Héctor D. ORCID logoORCID:, 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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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.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 28805
Notes on copyright: © The Author(s) 2016.
This article is published with open access at
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Depositing User: Hector Menendez Benito
Date Deposited: 02 Feb 2020 21:33
Last Modified: 25 Oct 2022 10:47

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