Clustering Optimisation Method for Highly Connected Biological Data. (arXiv:2208.04720v2 [q-bio.QM] UPDATED)

Currently, data-driven discovery in biological sciences resides in finding
segmentation strategies in multivariate data that produce sensible descriptions
of the data. Clustering is but one of several approaches and sometimes falls
short because of difficulties in assessing reasonable cutoffs, the number of
clusters that need to be formed or that an approach fails to preserve
topological properties of the original system in its clustered form. In this
work, we show how a simple metric for connectivity clustering evaluation leads
to …

arxiv bio clustering data