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

Menéndez, Héctor D. ORCID logoORCID: https://orcid.org/0000-0002-6314-3725, Otero, Fernando E. B. and Camacho, David ORCID logoORCID: https://orcid.org/0000-0002-5051-3475 (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. ISSN 1758-0366 [Article] (doi:10.1504/IJBIC.2017.085894)

PDF - Final accepted version (with author's formatting)
Download (472kB) | Preview


Data analysis has become an important field over the last decades. The growing amount of data demands new analytical methodologies in order to extract relevant knowledge. Clustering is one of the most competitive techniques in this context.Using a dataset as a starting point, these techniques aim to blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are the mostly used methodologies in this area, are however 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 clustering solutions and stability, specially for dense datasets. Ant Colony Optimization (ACO) is one of these new bio-inspired methodologies. This paper presents an extension of a previous algorithm named Spectral-based ACO Clustering (SACOC). SACOC is a spectral-based clustering methodology used for manifold identification. This work is focused on improving this algorithm through the Nystrom extension. The new algorithm, named SACON, is able to deal with Dense Data problems.We have evaluated the performance of this new approach comparing it with online clustering algorithms and the Nystrom extension of the Spectral Clustering algorithm using several datasets.

Item Type: Article
Keywords (uncontrolled): Ant colony optimization, clustering, data mining, machine learning, spectral, Nyström, SACON, SACOC
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 28800
Useful Links:
Depositing User: Hector Menendez Benito
Date Deposited: 02 Feb 2020 21:01
Last Modified: 29 Nov 2022 20:40
URI: https://eprints.mdx.ac.uk/id/eprint/28800

Actions (login required)

View Item View Item


Activity Overview
6 month trend
6 month trend

Additional statistics are available via IRStats2.