Clustering Optimisation Method for Highly Connected Biological Data
- URL: http://arxiv.org/abs/2208.04720v2
- Date: Thu, 11 Aug 2022 17:41:20 GMT
- Title: Clustering Optimisation Method for Highly Connected Biological Data
- Authors: Richard Tj\"ornhammar
- Abstract summary: We show how a simple metric for connectivity clustering evaluation leads to an optimised segmentation of biological data.
The novelty of the work resides in the creation of a simple optimisation method for clustering crowded data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 an optimised segmentation of biological data.
The novelty of the work resides in the creation of a simple optimisation
method for clustering crowded data. The resulting clustering approach only
relies on metrics derived from the inherent properties of the clustering. The
new method facilitates knowledge for optimised clustering, which is easy to
implement.
We discuss how the clustering optimisation strategy corresponds to the viable
information content yielded by the final segmentation. We further elaborate on
how the clustering results, in the optimal solution, corresponds to prior
knowledge of three different data sets.
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