Benchmark and application of unsupervised classification approaches for univariate data

Benchmark and application of unsupervised classification approaches for univariate data

El Abbassi, M. (TU Delft QN/van der Zant Lab; Kavli institute of nanoscience Delft)
Overbeck, Jan (Swiss Federal Laboratories for Materials Science and Technology (Empa); University of Basel)
Braun, Oliver (Swiss Federal Laboratories for Materials Science and Technology (Empa); University of Basel)
Calame, Michel (Swiss Federal Laboratories for Materials Science and Technology (Empa); University of Basel)
van der Zant, H.S.J. (TU Delft QN/van der Zant Lab; Kavli institute of nanoscience Delft)
Perrin, M.L. (Swiss Federal Laboratories for Materials Science and Technology (Empa))

2021

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.

http://resolver.tudelft.nl/uuid:2d899526-f8c2-47ec-b0ee-acd6a9f24889

https://doi.org/10.1038/s42005-021-00549-9

2399-3650

Communications Physics, 4 (1)

Institutional Repository

journal article

© 2021 M. El Abbassi, Jan Overbeck, Oliver Braun, Michel Calame, H.S.J. van der Zant, M.L. Perrin