An ensemble solution for multivariate time series clustering

Author:

Vázquez Iago,Villar José R.,Sedano Javier,Simić Svetlana,de la Cal Enrique

Funder

Gobierno del Principado de Asturias

Ministerio de Economía y Competitividad

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference45 articles.

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2. N.-A. Le-Khac, C. Fan, T. Kechadi, Clustering Approaches for Financial Data Analysis: a survey, in: 8th International conference on Data Mining (DMIN 2012), 2012, pp. 1–7.

3. S. Aghabozorgi, A. Seyed Shirkhorshidi, T. Ying Wah, Time-series clustering - A decade review, Information Systems 53 (2015) 16–38. URL https://www.sciencedirect.com/science/article/pii/S0306437915000733. 10.1016/j.is.2015.04.007.

4. COBRASTS: A New Approach to Semi-supervised Clustering of Time Series;Van Craenendonck,2018

5. A. Bagnall, M. Flynn, J. Large, J. Lines, M. Middlehurst, A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0, arXiv e-prints (2020). http://arxiv.org/abs/2004.06069.

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