Machine learning (ML) develops algorithms to identify patterns in data (unsupervised ML) or make predictions and inferences (supervised ML).

Unsupervised machine learning searches for structure in unlabeled data (data without a response variable). The goal of unsupervised learning is clustering into homogenous subgroups, and dimensionality reduction. Examples of cluster analysis are k-means clustering, hierarchical cluster analysis (HCA), and PCA (others here).