A second common situation is when a specific input-output relationship is not known in advance, in which case the model is "unsupervised" and tasked with finding relationships based on how data points are distributed relative to each other. Clustering problems identify data that aggregate together due to an underlying similarity in composition or processing (Fig4a). This is useful when analyzing datasets where points are expected to fall into few discrete categories (such as phases, domains, etc). Dimensionality reduction models seek to identify hidden or "latent" variables in data that describe structures and patterns of similarity. When correctly trained, the latent variables of a dataset can reconstruct the original without loss of information.