Clustering Techniques Selection for a Hybrid Regression Model: A Case
  Study Based on a Solar Thermal System
María Teresa García-Ordás Héctor Alaiz-Moretón José-Luis Casteleiro-Roca Esteban Jove José Alberto Benítez-Andrades Isaías García-Rodríguez Héctor Quintián José Luis Calvo-Rolle
This work addresses the performance comparison between four clustering
techniques with the objective of achieving strong hybrid models in supervised
learning tasks. A real dataset from a bio-climatic house named Sotavento placed
on experimental wind farm and located in Xermade (Lugo) in Galicia (Spain) has
been collected. Authors have chosen the thermal solar generation system in
order to study how works applying several cluster methods followed by a
regression technique to predict the output temperature of the system. With the
objective of defining the quality of each clustering method two possible
solutions have been implemented. The first one is based on three unsupervised
learning metrics (Silhouette, Calinski-Harabasz and Davies-Bouldin) while the
second one, employs the most common error measurements for a regression
algorithm such as Multi Layer Perceptron.