Unsupervised training is a type of Machine Learning (like supervised learning and reinforcement learning) in which the data output (class or numerical value) is not used during the algorithm, either because it is unknown or because of the type of solution to be obtained.
This type of training is useful for clustering or dimensionality reduction applications, where one seeks to automatically discover patterns, trends, objects, features, etc., of a population. A typical example of unsupervised training is used in Self-Organizing Maps (SOM).
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