TY - GEN
T1 - A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data
AU - Navajas-Guerrero, Adriana
AU - Manjarres, Diana
AU - Portillo, Eva
AU - Landa-Torres, Itziar
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
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.
KW - Clustering
KW - Harmony Search
KW - Internal parameters configuration
KW - Optimization
KW - Time series clustering
UR - http://www.scopus.com/inward/record.url?scp=85065918607&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20055-8_17
DO - 10.1007/978-3-030-20055-8_17
M3 - Conference contribution
AN - SCOPUS:85065918607
SN - 9783030200541
T3 - Advances in Intelligent Systems and Computing
SP - 179
EP - 189
BT - 14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019, Proceedings
A2 - Martínez Álvarez, Francisco
A2 - Troncoso Lora, Alicia
A2 - Sáez Muñoz, José António
A2 - Corchado, Emilio
A2 - Quintián, Héctor
PB - Springer Verlag
T2 - 14th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2019
Y2 - 13 May 2019 through 15 May 2019
ER -