2026-06-07T07:50:03Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/226412026-02-03T11:58:39Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Atencia-Ruiz, Miguel Alejandro
author
Gallicchio, Claudio
author
Joya-Caparrós, Gonzalo
author
Micheli, Alessio
2021-07-14T07:56:18Z
2021-07-14T07:56:18Z
2020
Atencia M., Gallicchio C., Joya G., Micheli A. (2020) Time Series Clustering with Deep Reservoir Computing. In: Farkaš I., Masulli P., Wermter S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science, vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_39
https://hdl.handle.net/10630/22641
This paper proposes a method for clustering of time series, based upon the ability of deep Reservoir Computing networks to grasp
the dynamical structure of the series that is presented as input. A standard clustering algorithm, such as k-means, is applied to the network states, rather than the input series themselves. Clustering is thus embedded into the network dynamical evolution, since a clustering result is obtained at every time step, which in turn serves as initialisation at the next step. We empirically assess the performance of deep reservoir systems in time series clustering on benchmark datasets, considering the influence of crucial hyperparameters. Experimentation with the proposed model shows enhanced clustering quality, measured by the silhouette coefficient, when compared to both static clustering of data, and dynamic clustering with a shallow network.
eng
Análisis cluster -- Programas de ordenador
Time Series Clustering with Deep Reservoir Computing
conference output
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URL
https://riuma.uma.es/bitstreams/451a3fd2-ebf0-499a-b7d1-2c042656a51f/download
File
MD5
0d637ba7c93c5d960754f7e541e8b5a2
686353
application/pdf
Clustering_with_Deep_RC.pdf