Extending the SACOC algorithm through the Nyström method for dense manifold data analysis. (2017)

• Record Type: Journal Article
• Authors: Menéndez, Héctor D.; Otero, Fernando E.B.; Camacho, David
• Is Part Of: International journal of bio-inspired computation. Volume 10:Number 2(2017)
• Page Start: 127
• Page End: 135
• Publication Date: 2017
• Subjects: ant colony optimisation -- ACO -- clustering -- data mining -- machine learning -- spectral -- Nyström -- SACON -- SACOC; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

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 Nyström extension for dealing with dense data problems. We evaluated the new approach, called SACON, comparing it against online clustering algorithms and the Nyström extension of spectral clustering.