SACOC: A spectral-based ACO clustering algorithm
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2015-01-01Citation
10.1007/978-3-319-10422-5_20
Intelligent Distributed Computing VIII. Part VI. Studies in Computational Intelligence, Volumen 570. Springer, 2015. 185-194.
ISSN
1860-949X (print); 1860-9503 (online)ISBN
978-3-319-10421-8 (print); 978-3-319-10422-5 (online)DOI
10.1007/978-3-319-10422-5_20Funded by
This 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).Project
Gobierno de España. TIN2010-19872Editor's Version
http://dx.doi.org/10.1007/978-3-319-10422-5_20Subjects
ACO; Clustering; Data Mining; Spectral; Informática; TelecomunicacionesNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10422-5_20Proceedings of Eight International Symposium on Intelligent Distributed Computing held in Madrid in September 3-5, 2014
Rights
© Springer-Verlag Berlin Heidelberg 2015Abstract
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, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest—an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experimentsmeasure the accuracy of the algorithm for both synthetic datasets and realworld datasets extracted from the UCI Machine Learning Repository.
Files in this item
Google Scholar:Menéndez, Héctor D.
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Otero, Fernando E. B.
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Camacho, David
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