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Extending the SACOC algorithm through the Nystrom method for Dense Manifold Data Analysis

Menendez, Hector, Otero, Fernando E.B., Camacho, David (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. E-ISSN 1758-0374. (doi:10.1504/IJBIC.2017.085894) (KAR id:53515)

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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, clustering techniques aim to 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 in this area, 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. Dense datasets are featured by areas of higher density, where there are significantly more data instances than in the rest of the search space. This paper presents an extension of a previous algorithm named Spectral-based Ant Colony Optimization Clustering (SACOC), a spectral-based clustering methodology used for manifold identification. This work focuses on improving the SACOC algorithm through the Nystrom extension in order to deal with dense data problems. We evaluated the performance of the proposed approach, called SACON, comparing it against online clustering algorithms and the Nystrom extension of the Spectral Clustering algorithm using several benchmark datasets.

Item Type: Article
DOI/Identification number: 10.1504/IJBIC.2017.085894
Uncontrolled keywords: Ant Colony Optimization, Clustering, Data Mining, Machine Learning, Spectral, Nyström, SACON, SACOC
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 17 Dec 2015 01:18 UTC
Last Modified: 01 Aug 2019 10:40 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
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