Unsupervised learning

Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data.^[1] Within such an approach, a machine learning model tries to find any similarities, differences, patterns, and structure in data by itself. No prior human intervention is needed.^[1]

Other methods in the supervision spectrum are Reinforcement Learning where the machine is given only a numerical performance score as guidance,^[2] and Weak or Semi supervision where a small portion of the data is tagged, and Self Supervision.

methods include: hierarchical clustering,^[11] k-means,^[12] mixture models, model-based clustering, DBSCAN, and OPTICS algorithm

Clustering

methods include: Local Outlier Factor, and Isolation Forest

Anomaly detection

Approaches for learning such as Expectation–maximization algorithm (EM), Method of moments, and Blind signal separation techniques (Principal component analysis, Independent component analysis, Non-negative matrix factorization, Singular value decomposition)

latent variable models

Unsupervised learning

Clustering

Anomaly detection

latent variable models

Automated machine learning

Cluster analysis

Model-based clustering

Anomaly detection

Expectation–maximization algorithm

Generative topographic map

Meta-learning (computer science)

Multivariate analysis

Radial basis function network

Weak supervision