In unsupervised learning, algorithms learn from unlabeled data by looking for hidden structures in the data.
Obtaining unlabeled data is comparatively inexpensive and unsupervised learning can be used to uncover very useful information in such data.
Types of Unsupervised Machine Learning
Clustering: organizes entities from the input data into a finite number of subsets or clusters
Feature Learning: transforms sets of inputs into other inputs that are potentially more useful in solving a given problem
Anomaly Detection: identifies two major groups of entities: 1) Normal, 2) Abnormal (anomalies)
Some other types include Dimensionality Reduction, Feature Extraction, Neural Networks, Principle Component Analysis, Matrix Factorization.