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
- Klíčová slovaKeywords
- clustering, hieararchical, biomedical, sleep, EEG
- KonferenceConference
- Precision Medicine Powered by pHealth and Connected Health (Thessaloniki, 2017-11-18)
- DOIDOI
- 10.1007/978-981-10-7419-6_1
- Časopis / citaceJournal / citation
- In: IFMBE Proceedings, Springer Singapore, 2018, pp. 3-7 · ISSN 1680-0737
- RokYear
- 2018
- JazykLanguage
- eng
- ZáznamyRecords
- ProjektProject
- Časový kontext v úloze analýzy dlouhodobého nestacionárního vícerozměrného signáluTemporal context in analysis of long-term non.stationary multidimensional signal
- CitovánoCited by
- 1 (OpenAlex)
- Citace ke staženíDownload citation
- TXT · BibTeX
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.