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

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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 languageEnglish
Title of host publicationWI '19: IEEE/WIC/ACM International Conference on Web Intelligence
Subtitle of host publicationProceedings
EditorsPayam Barnaghi, Georg Gottlob, Yannis Manolopoulos, Theodoros Tzouramanis, Athena Vakali
PublisherAssociation for Computing Machinery, Inc
Pages270-275
Number of pages6
ISBN (Electronic)9781450369343
DOIs
Publication statusPublished - Oct 2019
Event19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019 - Thessaloniki, Greece
Duration: 13 Oct 201917 Oct 2019

Conference

Conference19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019
CountryGreece
CityThessaloniki
Period13/10/1917/10/19

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Keywords

  • Clustering
  • E-Health
  • Online clustering
  • Time series

Cite this

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