RT Conference Proceedings
T1 A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data
A1 Navajas-Guerrero, Adriana
A1 Manjarres, Diana
A1 Portillo, Eva
A1 Landa-Torres, Itziar
A2 Martínez Álvarez, Francisco
A2 Troncoso Lora, Alicia
A2 Sáez Muñoz, José António
A2 Corchado, Emilio
A2 Quintián, Héctor
AB Clustering methods have become popular in the last years due to the need of analyzing the high amount of collected data from different fields of knowledge. Nevertheless, the main drawback of clustering is the selection of the optimal method along with its internal parameters in an unsupervised environment. In the present paper, a novel heuristic approach based on the Harmony Search algorithm aided with a local search procedure is presented for simultaneously optimizing the best clustering algorithm (K-means, DBSCAN and Hierarchical clustering) and its optimal internal parameters based on the Silhouette index. Extensive simulation results show that the presented approach outperforms the standard clustering configurations and also other works in the literature in different Time Series and synthetic databases.
PB Springer Verlag
SN 9783030200541
SN 2194-5357
YR 2020
FD 2020
LK https://hdl.handle.net/11556/2188
UL https://hdl.handle.net/11556/2188
LA eng
NO Navajas-Guerrero , A , Manjarres , D , Portillo , E & Landa-Torres , I 2020 , A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data . in F Martínez Álvarez , A Troncoso Lora , J A Sáez Muñoz , E Corchado & H Quintián (eds) , 14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019, Proceedings . Advances in Intelligent Systems and Computing , vol. 950 , Springer Verlag , pp. 179-189 , 14th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2019 , Seville , Spain , 13/05/19 . https://doi.org/10.1007/978-3-030-20055-8_17
NO conference
NO Publisher Copyright: © 2020, Springer Nature Switzerland AG.
NO Acknowledgments. This research has been supported by a TECNALIA Research and Innovation PhD Scholarship, ELKARTEK program (SENDANEU KK-2018/00032) and the HAZITEK program (DATALYSE ZL-2018/00765) of the Basque Government.
DS TECNALIA Publications
RD 28 jul 2024