Unsupervised learning is a machine learning technique where a set of input data is provided to the algorithm without labelling, i.e. without telling it what the expected output is. The aim of the algorithm is to identify underlying patterns or structures in the input data and to cluster them in a meaningful way. Unlike supervised learning, where the algorithm receives labelled data, in unsupervised learning the algorithm must find patterns and relationships in the data on its own. Common examples of unsupervised learning techniques are clustering and dimensionality reduction.
Clustering methods, or grouping, are a fundamental part of the data analysis process, since they allow an automatic segmentation of the data [...]
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