Hybrid Hierarchical Clustering Algorithm Used for Large Datasets: A Pilot Study on Long-Term Sleep Data
- NázevTitle
- Hybrid Hierarchical Clustering Algorithm Used for Large Datasets: A Pilot Study on Long-Term Sleep DataHybrid Hierarchical Clustering Algorithm Used for Large Datasets: A Pilot Study on Long-Term Sleep Data
- Druh výsledkuResult type
- Příspěvek ve sborníkuProceedings paper
- AutořiAuthors
- V. Gerla, M. Murgaš, A. Mládek, E. Saifutdinova, M. Macaš, L. Lhotská
- DOIDOI
- 10.1007/978-981-10-7419-6_1
- Časopis / citaceJournal / citation
- In: Precision Medicine Powered by pHealth and Connected Health. Springer Nature Singapore Pte Ltd., 2018. p. 3-7. 1. vol. 66. ISSN 1680-0737. ISBN 978-981-10-7418-9.
- JazykLanguage
- eng
- ScopusScopus
- 2-s2.0-85035316407
- RIVRIV
- RIV/68407700:21230/18:00315877!RIV19-GA0-21230___
- ProjektProject
- Časový kontext v úloze analýzy dlouhodobého nestacionárního vícerozměrného signáluTemporal context in analysis of long-term non.stationary multidimensional signal
AbstraktAbstract
The presented study proposes a new hybrid hierarchical clustering method suitable for large datasets. It is based on the combination of effective simple methods. The proposed method was tested and compared with a widely used agglomerative clustering method. Two groups of datasets were used for testing. The first group contains data delivered from real biomedical data and related to a real problem of indication of sleep stages. The second group consists of artificially generated large data. Time, memory consumption, and mutual information were compared.
The presented study proposes a new hybrid hierarchical clustering method suitable for large datasets. It is based on the combination of effective simple methods. The proposed method was tested and compared with a widely used agglomerative clustering method. Two groups of datasets were used for testing. The first group contains data delivered from real biomedical data and related to a real problem of indication of sleep stages. The second group consists of artificially generated large data. Time, memory consumption, and mutual information were compared.