Simon Crase1,2, Benjamin Hall2, Suresh N. Thennadil3,*
CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2435-2458, 2022, DOI:10.32604/cmc.2022.022414
Abstract Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy, namely, high dimensionality and small sample size. In order to improve cluster analysis outcomes, feature selection can be used to remove redundant or irrelevant features and reduce the dimensionality. However, for cluster analysis, this must be done in an unsupervised manner without the benefit of data labels. This paper presents a novel feature selection approach for cluster analysis, utilizing clusterability metrics to remove features that least contribute to a dataset's tendency to cluster. Two versions are presented and evaluated:… More >
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