A genetic graph-based approach for partitional clustering

Menéndez, Héctor D. ORCID logoORCID: https://orcid.org/0000-0002-6314-3725, Barrero, D.F. and Camacho, David ORCID logoORCID: https://orcid.org/0000-0002-5051-3475 (2014) A genetic graph-based approach for partitional clustering. International Journal of Neural Systems, 24 (3) , 1430008. ISSN 0129-0657 [Article] (doi:10.1142/S0129065714300083)

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Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 28818
Notes on copyright: Electronic version of an article published as International Journal of Neural Systems, 24(03), 2014, 1430008, 10.1142/S0129065714300083 © World Scientific Publishing Company https://www.worldscientific.com/worldscinet/ijns
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Depositing User: Hector Menendez Benito
Date Deposited: 02 Feb 2020 21:48
Last Modified: 29 Nov 2022 23:51
URI: https://eprints.mdx.ac.uk/id/eprint/28818

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