A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data

Adriana Navajas-Guerrero; Diana Manjarres; Eva Portillo; Itziar Landa-Torres

doi:10.1007/978-3-030-20055-8_17

A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data

Adriana Navajas-Guerrero^*, Diana Manjarres, Eva Portillo, Itziar Landa-Torres

^*Corresponding author for this work

Artificial Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019, Proceedings
Editors	Francisco Martínez Álvarez, Alicia Troncoso Lora, José António Sáez Muñoz, Emilio Corchado, Héctor Quintián
Publisher	Springer Verlag
Pages	179-189
Number of pages	11
ISBN (Print)	9783030200541
DOIs	https://doi.org/10.1007/978-3-030-20055-8_17
Publication status	Published - 2020
Event	14th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2019 - Seville, Spain Duration: 13 May 2019 → 15 May 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	950
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	14th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2019
Country/Territory	Spain
City	Seville
Period	13/05/19 → 15/05/19

Keywords

Clustering
Harmony Search
Internal parameters configuration
Optimization
Time series clustering

Access to Document

10.1007/978-3-030-20055-8_17

Cite this

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 (pp. 179-189). (Advances in Intelligent Systems and Computing; Vol. 950). Springer Verlag. https://doi.org/10.1007/978-3-030-20055-8_17

Navajas-Guerrero, Adriana ; Manjarres, Diana ; Portillo, Eva et al. / A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data. 14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019, Proceedings. editor / Francisco Martínez Álvarez ; Alicia Troncoso Lora ; José António Sáez Muñoz ; Emilio Corchado ; Héctor Quintián. Springer Verlag, 2020. pp. 179-189 (Advances in Intelligent Systems and Computing).

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title = "A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data",

abstract = "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.",

keywords = "Clustering, Harmony Search, Internal parameters configuration, Optimization, Time series clustering",

author = "Adriana Navajas-Guerrero and Diana Manjarres and Eva Portillo and Itziar Landa-Torres",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 14th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2019 ; Conference date: 13-05-2019 Through 15-05-2019",

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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, JA 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

A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data. / Navajas-Guerrero, Adriana ; Manjarres, Diana; Portillo, Eva et al.
14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019, Proceedings. ed. / Francisco Martínez Álvarez; Alicia Troncoso Lora; José António Sáez Muñoz; Emilio Corchado; Héctor Quintián. Springer Verlag, 2020. p. 179-189 (Advances in Intelligent Systems and Computing; Vol. 950).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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

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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 -

Navajas-Guerrero A , Manjarres D, Portillo E, Landa-Torres I. A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data. In Martínez Álvarez F, Troncoso Lora A, Sáez Muñoz JA, Corchado E, Quintián H, editors, 14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019, Proceedings. Springer Verlag. 2020. p. 179-189. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-20055-8_17

A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data

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