MACOC: A medoid-based ACO clustering algorithm
EntityUAM. Departamento de Ingeniería Informática
PublisherSpringer Berlin Heidelberg
10.1007/978-3-319-09952-1_11Swarm Intelligence: 9th International Conference, ANTS 2014, Brussels, Belgium, September 10-12, 2014. Proceedings. Lecture Notes in Computer Science, Volumen 8667. Springer, 2014. 122-133.
ISSN0302-9743 (print); 1611-3349 (online)
ISBN978-3-319-09951-4 (print); 978-3-319-09952-1 (online)
Funded byThis work has been partly supported by: Spanish Ministry of Science and Education under project TIN2010-19872 and Savier – an Airbus Defense & Space project (FUAM-076914 and FUAM-076915).
ProjectGobierno de España. TIN2010-19872
SubjectsAnt Colony Optimization; Clustering; Data Mining; Machine Learning; Medoid; Informática
NoteProceedings of 9th International Conference, ANTS 2014, Brussels, Belgium, September 10-12, 2014.
he final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-09952-1_11
Rights© Springer-Verlag Berlin Heidelberg 2014
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.
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