Comparative Analysis of Clustering Techniques for a Hybrid Model Implementation
- María Teresa García-Ordás 1
- Héctor Alaiz-Moretón 1
- José-Luis Casteleiro-Roca 2
- Esteban Jove 2
- José Alberto Benítez-Andrades 1
- Isaías García-Rodríguez 1
- Héctor Quintián 2
- José Luis Calvo-Rolle 2
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1
Universidad de León
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2
Universidade da Coruña
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- Álvaro Herrero (coord.)
- Carlos Cambra (coord.)
- Daniel Urda (coord.)
- Javier Sedano (coord.)
- Héctor Quintián (coord.)
- Emilio Corchado (coord.)
Publisher: Springer Suiza
ISBN: 978-3-030-57801-5, 978-3-030-57802-2
Year of publication: 2021
Pages: 355-365
Congress: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)
Type: Conference paper
Abstract
This research is oriented to compare the performance of two clustering methods in order to know what is the best one for archiving high quality hybrid models. For testing purposes, a real dataset has been obtained of a bio-climate house located in Sotavento Experimental Wind Farm, in Xermade (Lugo) in Galicia (Spain). Between several systems installed in the house, the thermal solar generation system has been the chosen one for studying its behaviour and experimenting with the clustering techniques.Two approaches have been utilized for establishing the quality of each clustering algorithm. The first one is based on typical error measurements implied in a regression procedure such as Multi Layer Perceptron. Whereas, the second one, is oriented to the utilization of three unsupervised learning metrics (Silhouette, Calinski-Harabasz and Davies-Bouldin).