A genetic graph-based approach for partitional clustering

Article


Menéndez, H., Barrero, D. and Camacho, D. 2014. A genetic graph-based approach for partitional clustering. International Journal of Neural Systems. 24 (3). https://doi.org/10.1142/S0129065714300083
TypeArticle
TitleA genetic graph-based approach for partitional clustering
AuthorsMenéndez, H., Barrero, D. and Camacho, D.
Abstract

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.

PublisherWorld Scientific Publishing Company
JournalInternational Journal of Neural Systems
ISSN0129-0657
Electronic1793-6462
Publication dates
Print24 Jan 2014
Publication process dates
Deposited02 Feb 2020
Accepted23 Dec 2013
Output statusPublished
Accepted author manuscript
Copyright Statement

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

Digital Object Identifier (DOI)https://doi.org/10.1142/S0129065714300083
LanguageEnglish
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