CluStream-GT: Online clustering for personalization in the health domain

Eoin Martino Grua; Mark Hoogendoorn; Ivano Malavolta; Patricia Lago; A. E. Eiben

doi:10.1145/3350546.3352529

CluStream-GT: Online clustering for personalization in the health domain

Eoin Martino Grua, Mark Hoogendoorn, Ivano Malavolta, Patricia Lago, A. E. Eiben

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

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Abstract

Clustering of users underlies many of the personalisation algorithms that are in use nowadays. Such clustering is mostly performed in an offline fashion. For a health and wellbeing setting, offline clustering might however not be suitable, as limited data is often available and patient states can also quickly evolve over time. Existing online clustering algorithms are not suitable for the health domain due to the type of data that involves multiple time series evolving over time. In this paper we propose a new online clustering algorithm called CluStream-GT that is suitable for health applications. By using both artificial and real datasets, we show that the approach is far more efficient compared to regular clustering, with an average speedup of 93%, while only losing 12% in the accuracy of the clustering with artificial data and 3% with real data.

Original language	English
Title of host publication	WI '19: IEEE/WIC/ACM International Conference on Web Intelligence
Subtitle of host publication	Proceedings
Editors	Payam Barnaghi, Georg Gottlob, Yannis Manolopoulos, Theodoros Tzouramanis, Athena Vakali
Publisher	Association for Computing Machinery, Inc
Pages	270-275
Number of pages	6
ISBN (Electronic)	9781450369343
DOIs	https://doi.org/10.1145/3350546.3352529
Publication status	Published - Oct 2019
Event	19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019 - Thessaloniki, Greece Duration: 13 Oct 2019 → 17 Oct 2019

Conference

Conference	19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019
Country/Territory	Greece
City	Thessaloniki
Period	13/10/19 → 17/10/19

Keywords

Clustering
E-Health
Online clustering
Time series

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10.1145/3350546.3352529

CluStreamGT_Online clustering for personalization in the health domainFinal published version, 731 KBLicence: Article 25fa Dutch Copyright Act

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Grua, E. M., Hoogendoorn, M., Malavolta, I., Lago, P., & Eiben, A. E. (2019). CluStream-GT: Online clustering for personalization in the health domain. In P. Barnaghi, G. Gottlob, Y. Manolopoulos, T. Tzouramanis, & A. Vakali (Eds.), WI '19: IEEE/WIC/ACM International Conference on Web Intelligence: Proceedings (pp. 270-275). Association for Computing Machinery, Inc. https://doi.org/10.1145/3350546.3352529

Grua, Eoin Martino ; Hoogendoorn, Mark ; Malavolta, Ivano et al. / CluStream-GT: Online clustering for personalization in the health domain. WI '19: IEEE/WIC/ACM International Conference on Web Intelligence: Proceedings. editor / Payam Barnaghi ; Georg Gottlob ; Yannis Manolopoulos ; Theodoros Tzouramanis ; Athena Vakali. Association for Computing Machinery, Inc, 2019. pp. 270-275

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abstract = "Clustering of users underlies many of the personalisation algorithms that are in use nowadays. Such clustering is mostly performed in an offline fashion. For a health and wellbeing setting, offline clustering might however not be suitable, as limited data is often available and patient states can also quickly evolve over time. Existing online clustering algorithms are not suitable for the health domain due to the type of data that involves multiple time series evolving over time. In this paper we propose a new online clustering algorithm called CluStream-GT that is suitable for health applications. By using both artificial and real datasets, we show that the approach is far more efficient compared to regular clustering, with an average speedup of 93%, while only losing 12% in the accuracy of the clustering with artificial data and 3% with real data.",

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author = "Grua, {Eoin Martino} and Mark Hoogendoorn and Ivano Malavolta and Patricia Lago and Eiben, {A. E.}",

year = "2019",

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booktitle = "WI '19: IEEE/WIC/ACM International Conference on Web Intelligence",

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Grua, EM, Hoogendoorn, M , Malavolta, I , Lago, P & Eiben, AE 2019, CluStream-GT: Online clustering for personalization in the health domain. in P Barnaghi, G Gottlob, Y Manolopoulos, T Tzouramanis & A Vakali (eds), WI '19: IEEE/WIC/ACM International Conference on Web Intelligence: Proceedings. Association for Computing Machinery, Inc, pp. 270-275, 19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019, Thessaloniki, Greece, 13/10/19. https://doi.org/10.1145/3350546.3352529

CluStream-GT: Online clustering for personalization in the health domain. / Grua, Eoin Martino; Hoogendoorn, Mark ; Malavolta, Ivano et al.
WI '19: IEEE/WIC/ACM International Conference on Web Intelligence: Proceedings. ed. / Payam Barnaghi; Georg Gottlob; Yannis Manolopoulos; Theodoros Tzouramanis; Athena Vakali. Association for Computing Machinery, Inc, 2019. p. 270-275.

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

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AU - Lago, Patricia

AU - Eiben, A. E.

PY - 2019/10

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N2 - Clustering of users underlies many of the personalisation algorithms that are in use nowadays. Such clustering is mostly performed in an offline fashion. For a health and wellbeing setting, offline clustering might however not be suitable, as limited data is often available and patient states can also quickly evolve over time. Existing online clustering algorithms are not suitable for the health domain due to the type of data that involves multiple time series evolving over time. In this paper we propose a new online clustering algorithm called CluStream-GT that is suitable for health applications. By using both artificial and real datasets, we show that the approach is far more efficient compared to regular clustering, with an average speedup of 93%, while only losing 12% in the accuracy of the clustering with artificial data and 3% with real data.

AB - Clustering of users underlies many of the personalisation algorithms that are in use nowadays. Such clustering is mostly performed in an offline fashion. For a health and wellbeing setting, offline clustering might however not be suitable, as limited data is often available and patient states can also quickly evolve over time. Existing online clustering algorithms are not suitable for the health domain due to the type of data that involves multiple time series evolving over time. In this paper we propose a new online clustering algorithm called CluStream-GT that is suitable for health applications. By using both artificial and real datasets, we show that the approach is far more efficient compared to regular clustering, with an average speedup of 93%, while only losing 12% in the accuracy of the clustering with artificial data and 3% with real data.

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Grua EM, Hoogendoorn M , Malavolta I , Lago P , Eiben AE. CluStream-GT: Online clustering for personalization in the health domain. In Barnaghi P, Gottlob G, Manolopoulos Y, Tzouramanis T, Vakali A, editors, WI '19: IEEE/WIC/ACM International Conference on Web Intelligence: Proceedings. Association for Computing Machinery, Inc. 2019. p. 270-275 Epub 2019 Oct 14. doi: 10.1145/3350546.3352529

CluStream-GT: Online clustering for personalization in the health domain

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