Unsupervised ML methods have been a mainstay analysis tool for data visualization in complex datasets. The uniting assumption behind all unsupervised learning problems is that data within an experiment or domain will follow common patterns that are independent of noise and specific outliers. This implies that data can be disentangled from those outliers and noise. Applications in XRD analysis enabled by this approach include improving signal-to-noise ratio , identifying buried peaks 103 , identifying outlier patterns 104 , and identifying outlier detector pixels 105 . A second implication of this assumption is that some features of the data are redundant: indeed, an N-point spectra is condensed to only a few parameters (lattice constants, orientation, microstrain, etc.) after Rietveld refinement or similar analysis. Hence, unsupervised methods are also often used as feature-extractors, which visualize trends in data that are obscured by their high dimensionality.