NDLI: Medoid-based clustering using ant colony optimization

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Medoid-based clustering using ant colony optimization

Content Provider	Springer Nature Link
Author	Menéndez, Héctor D. Otero, Fernando E. B. Camacho, David
Copyright Year	2016
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.
Starting Page	123
Ending Page	145
Page Count	23
File Format	PDF
ISSN	19353812
Journal	Swarm Intelligence
Volume Number	10
Issue Number	2
e-ISSN	19353820
Language	English
Publisher	Springer US
Publisher Date	2016-05-09
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ant colony optimization Clustering Data mining Machine learning Medoid Adaptive Artificial Intelligence (incl. Robotics) Computer Systems Organization and Communication Networks ApplicationMathematics/Computational Methods of Engineering Communications Engineering, Networks Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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