||Hierarchical clustering is a widely employed iterative method for the identification of subcategories
in large and multivariate datasets and the resulting hierarchical structure is often
visualized in the form of dendrogram-type representations. Traditional hierarchical clustering
is, however, not designed to optimize categorization using a flexible performance criterion.
A framework for multisectional and optimized hierarchical clustering (MOHIC) is outlined.
At each partitioning level (of a cluster into subclusters), MOHIC aims to optimize the
subcluster configuration as regards both number of subclusters, as well as their composition.
Initiation is performed with k-means clustering, whereas optimization procedure relies on
silhouette analysis. Outputs are presented in the form of dendrograms with several features to
support interpretation of the hierarchical structure. Results from three separate areas in
biomedicine are presented to demonstrate applicability of the novel algorithm.