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 (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.

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 Springerlink.com.
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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: 05 Feb 2020 10:23
URI: https://eprints.mdx.ac.uk/id/eprint/28805

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