Expand description
Clustering algorithms.
Includes K-Means, DBSCAN, Hierarchical, Gaussian Mixture Models, and Isolation Forest.
Structs§
- Agglomerative
Clustering - Agglomerative (bottom-up) hierarchical clustering.
- DBSCAN
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
- Gaussian
Mixture - Gaussian Mixture Model (GMM) for probabilistic clustering.
- Isolation
Forest - Isolation Forest for anomaly detection.
- KMeans
- K-Means clustering algorithm.
- Local
Outlier Factor - Local Outlier Factor (LOF) for density-based anomaly detection.
- Merge
- Dendrogram merge record.
- Spectral
Clustering - Spectral Clustering using graph Laplacian and eigendecomposition.
Enums§
- Affinity
- Affinity types for constructing similarity graphs.
- Covariance
Type - Covariance matrix types for Gaussian Mixture Models.
- Linkage
- Linkage methods for hierarchical clustering.