An analysis framework for clustering algorithm selection with applications to spectroscopy.

ABSTRACT: Cluster analysis is a valuable unsupervised machine learning technique that is applied in a multitude of domains to identify similarities or clusters in unlabelled data. However, its performance is dependent of the characteristics of the data it is being applied to. There is no universally best clustering algorithm, and hence, there are numerous clustering algorithms available with different performance characteristics. This raises the problem of how to select an appropriate clustering algorithm for the given analytical purposes. We present and validate an analysis framework to address this problem. Unlike most current literature which focuses on characterizing the clustering algorithm itself, we present a wider holistic approach, with a focus on the user's needs, the data's characteristic

SUBMITTER: Crase S 

PROVIDER: S-EPMC8970496 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature