SACOC: A spectral-based ACO clustering algorithm
EntityUAM. Departamento de Ingeniería Informática
PublisherSpringer Berlin Heidelberg
10.1007/978-3-319-10422-5_20Intelligent Distributed Computing VIII. Part VI. Studies in Computational Intelligence, Volumen 570. Springer, 2015. 185-194.
ISSN1860-949X (print); 1860-9503 (online)
ISBN978-3-319-10421-8 (print); 978-3-319-10422-5 (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
SubjectsACO; Clustering; Data Mining; Spectral; Informática; Telecomunicaciones
NoteThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10422-5_20
Proceedings of Eight International Symposium on Intelligent Distributed Computing held in Madrid in September 3-5, 2014
Rights© Springer-Verlag Berlin Heidelberg 2015
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.
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