Menéndez, HD;
Otero, FEB;
Camacho, D;
(2016)
Medoid-based clustering using ant colony optimization.
Swarm Intelligence
, 10
(2)
pp. 123-145.
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.
| Type: | Article |
|---|---|
| Title: | Medoid-based clustering using ant colony optimization |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s11721-016-0122-5 |
| Publisher version: | http://doi.org/10.1007/s11721-016-0122-5 |
| Language: | English |
| Additional information: | Copyright © The Author(s), 2016. All rights reserved. This is an Open Access article made available under a Creative Commons Attribution Non-commercial Non-derivative 4.0 International license (CC BY-NC-ND 4.0). This license allows you to share, copy, distribute and transmit the work for personal and non-commercial use providing author and publisher attribution is clearly stated. Further details about CC BY licenses are available at http://creativecommons.org/licenses/by/4.0 Access may be initially restricted by the publisher. |
| Keywords: | Ant colony optimization, Clustering, Data mining, Machine learning, Medoid, Adaptive |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1508524 |
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