Application of Agglomerative Hierarchical Clustering for Clustering of Time Series Data

Ana Radovanovic; Junshi Li; Jovica V. Milanovic; Nina Milosavljevic; Riccardo Storchi

doi:10.1109/ISGT-Europe47291.2020.9248759

Application of Agglomerative Hierarchical Clustering for Clustering of Time Series Data

Ana Radovanovic
, Junshi Li
, Jovica V. Milanovic
, Nina Milosavljevic
, Riccardo Storchi

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

511 Downloads (Pure)

Abstract

The advancements in technology have made it possible to automatically record and store large amount of data, which has resulted in a need for development and application of efficient data analysis techniques. Unsupervised data clustering methods have proven to be capable of extracting useful information from various types and sizes of datasets. This paper investigates the performance of the standard agglomerative hierarchical clustering algorithm using two time series datasets from electric power system and neuroscience area. The main steps in clustering procedure are presented in detail. Results show that the effectiveness of the clustering algorithm is affected to a large extent by the main characteristics of the clustering data and algorithm’s parameters.

Original language	English
Title of host publication	Proceedings of 2020 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020
Place of Publication	New York
Publisher	IEEE
Pages	640-644
Number of pages	5
ISBN (Electronic)	9781728171005
DOIs	https://doi.org/10.1109/ISGT-Europe47291.2020.9248759 https://doi.org/10.1109/isgt-europe47291.2020.9248759
Publication status	Published - 26 Oct 2020
Event	2020 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) - Virtual, The Hague, Netherlands Duration: 26 Oct 2020 → 28 Oct 2020 https://ieee-isgt-europe.org

Publication series

Name	IEEE PES Innovative Smart Grid Technologies Conference Europe
Volume	2020-October

Conference

Conference	2020 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)
Abbreviated title	ISGT Europe 2020
Country/Territory	Netherlands
City	The Hague
Period	26/10/20 → 28/10/20
Internet address	https://ieee-isgt-europe.org

Keywords

Clustering
Electric power system
Hierarchical clustering algorithm
Neuroscience

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Application of Agglomerative Hierarchical Clustering Algorithm for Clustering of Time Series Data-Pure

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Radovanovic, A., Li, J., Milanovic, J. V., Milosavljevic, N., & Storchi, R. (2020). Application of Agglomerative Hierarchical Clustering for Clustering of Time Series Data. In Proceedings of 2020 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020 (pp. 640-644). (IEEE PES Innovative Smart Grid Technologies Conference Europe; Vol. 2020-October). IEEE. https://doi.org/10.1109/ISGT-Europe47291.2020.9248759, https://doi.org/10.1109/isgt-europe47291.2020.9248759

@inproceedings{4cd0ce1ae07e43789a998f0b3cc6772f,

title = "Application of Agglomerative Hierarchical Clustering for Clustering of Time Series Data",

abstract = "The advancements in technology have made it possible to automatically record and store large amount of data, which has resulted in a need for development and application of efficient data analysis techniques. Unsupervised data clustering methods have proven to be capable of extracting useful information from various types and sizes of datasets. This paper investigates the performance of the standard agglomerative hierarchical clustering algorithm using two time series datasets from electric power system and neuroscience area. The main steps in clustering procedure are presented in detail. Results show that the effectiveness of the clustering algorithm is affected to a large extent by the main characteristics of the clustering data and algorithm{\textquoteright}s parameters.",

keywords = "Clustering, Electric power system, Hierarchical clustering algorithm, Neuroscience",

author = "Ana Radovanovic and Junshi Li and Milanovic, \{Jovica V.\} and Nina Milosavljevic and Riccardo Storchi",

note = "Funding Information: ACKNOWLEDGMENT AND DISCLAIMER The research is supported by the EU H2020 project CROSSBOW (grant agreement 773430), Fight for Sight Fellowship 5047/5048 and National Centre for Replacement Refinement and Reduction of Animals in Research (NC3Rs) via a David Sainsbury Fellowship (NC/P001505/1). The paper reflects only the authors{\textquoteright} views and neither the Agency nor the Commission are responsible for any use that may be made of the information contained therein. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), ISGT Europe 2020 ; Conference date: 26-10-2020 Through 28-10-2020",

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Radovanovic, A, Li, J, Milanovic, JV , Milosavljevic, N & Storchi, R 2020, Application of Agglomerative Hierarchical Clustering for Clustering of Time Series Data. in Proceedings of 2020 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020. IEEE PES Innovative Smart Grid Technologies Conference Europe, vol. 2020-October, IEEE, New York, pp. 640-644, 2020 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), The Hague, Netherlands, 26/10/20. https://doi.org/10.1109/ISGT-Europe47291.2020.9248759, https://doi.org/10.1109/isgt-europe47291.2020.9248759

Application of Agglomerative Hierarchical Clustering for Clustering of Time Series Data. / Radovanovic, Ana; Li, Junshi; Milanovic, Jovica V. et al.
Proceedings of 2020 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020. New York: IEEE, 2020. p. 640-644 (IEEE PES Innovative Smart Grid Technologies Conference Europe; Vol. 2020-October).

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

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N1 - Funding Information: ACKNOWLEDGMENT AND DISCLAIMER The research is supported by the EU H2020 project CROSSBOW (grant agreement 773430), Fight for Sight Fellowship 5047/5048 and National Centre for Replacement Refinement and Reduction of Animals in Research (NC3Rs) via a David Sainsbury Fellowship (NC/P001505/1). The paper reflects only the authors’ views and neither the Agency nor the Commission are responsible for any use that may be made of the information contained therein. Publisher Copyright: © 2020 IEEE.

PY - 2020/10/26

Y1 - 2020/10/26

N2 - The advancements in technology have made it possible to automatically record and store large amount of data, which has resulted in a need for development and application of efficient data analysis techniques. Unsupervised data clustering methods have proven to be capable of extracting useful information from various types and sizes of datasets. This paper investigates the performance of the standard agglomerative hierarchical clustering algorithm using two time series datasets from electric power system and neuroscience area. The main steps in clustering procedure are presented in detail. Results show that the effectiveness of the clustering algorithm is affected to a large extent by the main characteristics of the clustering data and algorithm’s parameters.

AB - The advancements in technology have made it possible to automatically record and store large amount of data, which has resulted in a need for development and application of efficient data analysis techniques. Unsupervised data clustering methods have proven to be capable of extracting useful information from various types and sizes of datasets. This paper investigates the performance of the standard agglomerative hierarchical clustering algorithm using two time series datasets from electric power system and neuroscience area. The main steps in clustering procedure are presented in detail. Results show that the effectiveness of the clustering algorithm is affected to a large extent by the main characteristics of the clustering data and algorithm’s parameters.

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KW - Electric power system

KW - Hierarchical clustering algorithm

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M3 - Conference contribution

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EP - 644

BT - Proceedings of 2020 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020

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Radovanovic A, Li J, Milanovic JV , Milosavljevic N , Storchi R. Application of Agglomerative Hierarchical Clustering for Clustering of Time Series Data. In Proceedings of 2020 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020. New York: IEEE. 2020. p. 640-644. (IEEE PES Innovative Smart Grid Technologies Conference Europe). doi: 10.1109/ISGT-Europe47291.2020.9248759, 10.1109/isgt-europe47291.2020.9248759

Application of Agglomerative Hierarchical Clustering for Clustering of Time Series Data

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Electromagnetic Sensing Group

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Application of Agglomerative Hierarchical Clustering for Clustering of Time Series Data

Abstract

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Electromagnetic Sensing Group

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