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

Article


Menéndez, H., Otero, F. and 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. https://doi.org/10.1504/IJBIC.2017.085894
TypeArticle
TitleExtending the SACOC algorithm through the Nystrom method for dense manifold data analysis
AuthorsMenéndez, H., Otero, F. and Camacho, D.
Abstract

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.

KeywordsAnt colony optimization, clustering, data mining, machine learning, spectral, Nyström, SACON, SACOC
LanguageEnglish
PublisherInderscience Publishers
JournalInternational Journal of Bio-Inspired Computation
ISSN1758-0366
Electronic1758-0374
Publication dates
Online28 Jul 2017
Print01 Sep 2017
Publication process dates
Deposited02 Feb 2020
Accepted01 Aug 2017
Output statusPublished
Accepted author manuscript
Digital Object Identifier (DOI)https://doi.org/10.1504/IJBIC.2017.085894
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