Medoid-based clustering using ant colony optimization |
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Authors: | Héctor D Menéndez Fernando E B Otero David Camacho |
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Institution: | 1.Department of Computer Science,University College London,London,UK;2.School of Computing,University of Kent,Kent,UK;3.Department of Computer Science,Universidad Autónoma de Madrid,Madrid,Spain |
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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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