Machine Learning
◎ Clustering
Finding hidden structure in unlabelled data — partition, hierarchy, density, and probabilistic approaches.
- 1→
K-Means Clustering
Partitioning data into k clusters by iteratively assigning points to the nearest centroid and re-computing centroids until convergence.
- 2→
Hierarchical Clustering
Building a tree of nested clusters — either by merging (agglomerative) or splitting (divisive) — visualized as a dendrogram.
- 3→
DBSCAN
Density-based clustering that groups densely packed points and marks sparse regions as noise — no need to specify k in advance.
- 4→
Gaussian Mixture Models
Modelling data as a weighted sum of Gaussian distributions, fitted by the EM algorithm — soft clustering with probabilistic assignments.
- 5→
Cluster Evaluation
Measuring clustering quality without labels — the elbow method, silhouette score, and Davies-Bouldin index.