Benchmark and application of unsupervised classification approaches for univariate data

Abstract

In the field of nanoscience, clustering methods have gained momentum for the analysis of experimental datasets with the aim of uncovering new physical properties. Here, the authors describe an unsupervised machine learning methodology that selects the optimal combination of feature space, clustering method, and number of clusters for the analysis of a range of experimental datasets, including break-junction traces, I-V curves, and Raman spectra.