Skip to main content

SACOC: A spectral-based ACO clustering algorithm

Menendez, Hector and Otero, Fernando E.B. and Camacho, David (2014) SACOC: A spectral-based ACO clustering algorithm. In: Intelligent Distributed Computing VIII. Studies in Computational Intelligence . Springer, Cham, Switzerland, pp. 185-194. ISBN 978-3-319-10421-8. E-ISBN 978-3-319-10422-5. (doi:10.1007/978-3-319-10422-5_20) (KAR id:42145)

PDF Author's Accepted Manuscript
Language: English
Download (356kB) Preview
[thumbnail of menendez-idc2014_preprint.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL


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 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: 10.1007/978-3-319-10422-5_20
Uncontrolled keywords: Clustering; Data Mining; ACO; Spectral
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 19:51 UTC
Last Modified: 16 Feb 2021 12:54 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
  • Depositors only (login required):


Downloads per month over past year