Unsupervised Learning
Clustering, dimensionality reduction, and anomaly detection.
Anomaly Detection
Identifying data points that deviate significantly from the norm – isolation forests, autoencoders, and statistical approaches.
Association Rules
Discovering frequent itemsets and co-occurrence patterns in transactional data – the Apriori algorithm and market basket analysis.
DBSCAN
Discovering arbitrarily-shaped clusters based on point density – no need to specify K, naturally identifies outliers.
Gaussian Mixture Models
Soft clustering via a weighted sum of Gaussians fitted with EM – probabilistic assignment captures cluster uncertainty.
Hierarchical Clustering
Building a tree of nested clusters via agglomerative merging or divisive splitting – revealing multi-scale data structure.
K-Means Clustering
Partitioning data into K groups by iteratively assigning points to nearest centroids – simple, fast, and surprisingly effective.
Principal Component Analysis
Projecting data onto orthogonal directions of maximum variance – the foundational dimensionality reduction technique.
t-SNE and UMAP
Nonlinear dimensionality reduction for visualization – preserving local neighborhood structure in 2D/3D plots.