Editing Unsupervised Learning and Clustering (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Unsupervised learning''' — Learning patterns from data without labels or target outputs.
* '''Clustering''' — Partitioning data into groups (clusters) where items within a group are more similar to each other than to items in other groups.
* '''K-means''' — A centroid-based clustering algorithm that iteratively assigns points to the nearest of K cluster centers and updates centers.
* '''Hierarchical clustering''' — Builds a tree (dendrogram) of nested clusters; agglomerative (bottom-up) or divisive (top-down).
* '''DBSCAN (Density-Based Spatial Clustering of Applications with Noise)''' — Finds clusters as dense regions separated by low-density areas; can discover arbitrarily shaped clusters.
* '''Gaussian Mixture Model (GMM)''' — A probabilistic model representing the data as a mixture of K Gaussian distributions; fit by EM algorithm.
* '''Expectation-Maximization (EM)''' — An iterative algorithm for fitting latent variable models; alternates between expectation (compute responsibility) and maximization (update parameters).
* '''PCA (Principal Component Analysis)''' — Linear dimensionality reduction that finds the directions of maximum variance.
* '''t-SNE''' — A non-linear dimensionality reduction for visualization; preserves local neighborhood structure.
* '''UMAP''' — Faster, more scalable alternative to t-SNE for visualization; better preserves global structure.
* '''Autoencoder''' — A neural network trained to reconstruct its input through a bottleneck; the bottleneck gives a compressed representation.
* '''Silhouette score''' — A clustering quality metric measuring how similar a point is to its own cluster vs. other clusters; range [-1, 1].
* '''Elbow method''' — Heuristic for choosing K in K-means: plot inertia vs. K, choose the "elbow" where improvement slows.
* '''Inertia''' — Within-cluster sum of squared distances from each point to its cluster center; K-means minimizes this.
</div>

<div style="background-color: #006400; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">