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MACOC: a medoid-based ACO clustering algorithm

Menendez, Hector and Otero, Fernando E.B. and Camacho, David (2014) MACOC: a medoid-based ACO clustering algorithm. In: Swarm Intelligence 9th International Conference. Lecture Notes in Computer Science . Springer, Cham, Switzerland, pp. 122-133. ISBN 978-3-319-09951-4. E-ISBN 978-3-319-09952-1. (doi:10.1007/978-3-319-09952-1_11) (KAR id:42146)

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The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-319-09952-1_11
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 07 Aug 2014 19:53 UTC
Last Modified: 16 Feb 2021 12:54 UTC
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