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Hybrid Hierarchical Clustering Algorithm Used for Large Datasets: A Pilot Study on Long-Term Sleep Data

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Precision Medicine Powered by pHealth and Connected Health (ICBHI 2017)

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Clustering is a popular analysis technique in a modern science full of unlabeled data, hidden dependencies and relations between elements in datasets. 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.

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This research has been supported by the project Temporal context in analysis of long-term non-stationary multidimensional signal, register number 17-20480S of the Grant Agency of the Czech Republic.

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Correspondence to V. Gerla .

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Gerla, V., Murgas, M., Mladek, A., Saifutdinova, E., Macas, M., Lhotska, L. (2018). Hybrid Hierarchical Clustering Algorithm Used for Large Datasets: A Pilot Study on Long-Term Sleep Data. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds) Precision Medicine Powered by pHealth and Connected Health. ICBHI 2017. IFMBE Proceedings, vol 66. Springer, Singapore.

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