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

摘要

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.