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In unsupervised learning, a set of data can be provided for example along with a cost function to be minimized, which can be a function of the data and the network output. The cost function can be dependent on the task and the properties of the parameters and observed variables or data. Unsupervised learning can be applied, for example, to pattern recognition, classification, and regression, general estimation problems, clustering, the estimation of statistical distributions, compression and filtering. Unsupervised learning can use one or more ANNs, expectation-maximization algorithms, data clustering, and the like. Association rule learning can use a priori algorithms, eclat algorithms, FP-growth algorithms, hierarchical clustering (e.g., single-linkage clustering and conceptual clustering), partitional clustering (e.g., K-means algorithm, fuzzy clustering), reinforcement learning (e.g., Monte Carlo method, Q-learning, temporal difference learning, and combinations thereof.

Added by DJM Jan 2024