Clustering
Unsupervised learning algorithms can be used for clustering, where the goal is to partition the data into groups or clusters based on similarity or proximity.
Dimensionality Reduction
Unsupervised learning techniques such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) are used for dimensionality reduction, where the goal is to reduce the number of features while preserving the essential structure of the data.
Anomaly Detection
Unsupervised learning algorithms can also be used for anomaly detection, where the goal is to identify rare or unusual instances in the data that deviate from normal behaviour.
Association Rule Learning
Association rule learning algorithms, such as Apriori and FP-growth, are used to discover interesting patterns or associations in large datasets, such as market basket analysis in retail.