  Unsupervised machine learning, and in particular data clustering, is a
powerful approach for the analysis of datasets and identification of
characteristic features occurring throughout a dataset. It is gaining
popularity across scientific disciplines and is particularly useful for
applications without a priori knowledge of the data structure. Here, we
introduce an approach for unsupervised data classification of any dataset
consisting of a series of univariate measurements. It is therefore ideally
suited for a wide range of measurement types. We apply it to the field of
nanoelectronics and spectroscopy to identify meaningful structures in data
sets. We also provide guidelines for the estimation of the optimum number of
clusters. In addition, we have performed an extensive benchmark of novel and
existing machine learning approaches and observe significant performance
differences. Careful selection of the feature space construction method and
clustering algorithms for a specific measurement type can therefore greatly
improve classification accuracies.
