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Medoid-based clustering using ant colony optimization

Menendez, Hector, Otero, Fernando E.B., Camacho, David (2016) Medoid-based clustering using ant colony optimization. Swarm Intelligence, 10 (2). pp. 123-145. ISSN 1935-3812. E-ISSN 1935-3820. (doi:10.1007/s11721-016-0122-5)

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
DOI/Identification number: 10.1007/s11721-016-0122-5
Uncontrolled keywords: Ant colony optimization Clustering Data mining Machine learning Medoid Adaptive
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 11 May 2016 11:43 UTC
Last Modified: 01 Aug 2019 10:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/55374 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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