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Extending the SACOC algorithm through the Nystrom method for dense manifold data analysis

Menéndez, HD; Otero, FEB; Camacho, D; (2017) Extending the SACOC algorithm through the Nystrom method for dense manifold data analysis. International Journal of Bio-Inspired Computation , 10 (2) pp. 127-135. 10.1504/IJBIC.2017.085894. Green open access

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Abstract

The growing amount of data demands new analytical methodologies to extract relevant knowledge. Clustering is one of the most competitive techniques in this context. Using a dataset as a starting point, clustering techniques blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are one of the main used methodologies, are sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the cluster selection, in particular for dense datasets, featured by areas of higher density. This paper extends a previous algorithm named spectral-based ant colony optimisation clustering (SACOC), used for manifold identification. We focus on improving it through the Nystrom extension for dealing with dense data problems. We evaluated the new approach, called SACON, comparing it against online clustering algorithms and the Nystrom extension of spectral clustering.

Type: Article
Title: Extending the SACOC algorithm through the Nystrom method for dense manifold data analysis
Open access status: An open access version is available from UCL Discovery
DOI: 10.1504/IJBIC.2017.085894
Publisher version: http://dx.doi.org/10.1504/IJBIC.2017.085894
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Ant Colony Optimization, Clustering, Data Mining, Machine Learning, Spectral, Nystr¨om, SACON, SACOC
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10060899
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