2020/04/30 by Maria El Abbassi, Jan Overbeck, Oliver Braun +3
Computer Science · Engineering · Materials Science · Mathematics · Physics and Astronomy · #A priori and a posteriori #Advanced Chemical Sensor Technologies #Artificial intelligence #Benchmark (surveying) #Cluster analysis #Computer science #Data mining #Data point #Data type #Feature selection #Field (mathematics) #Identification (biology) #Machine Learning in Materials Science #Machine learning #Mathematics #Multivariate statistics #Neural Networks and Applications #Pattern recognition (psychology) #Range (aeronautics) #Univariate #Unsupervised learning #cond-mat.mes-hall
paper · pdf · doi:10.1038/s42005-021-00549-9
openalex created_date 2021/01/18 · openalex publication_date 2021/03/12 · arxiv created 2021/03/22 · arxiv updated 2021/03/23 · openalex updated_date 2026/08/05
Abstract 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.