Unsupervised Learning

K-Means, DBSCAN, GMM, PCA, LDA, and hierarchical clustering

1.

DBSCAN

Density-based clustering for arbitrary cluster shapes

2.

Gaussian Mixture Models

Soft clustering using EM algorithm with Gaussian distributions

3.

HDBSCAN

Hierarchical density-based clustering that handles varying densities

4.

Hierarchical Clustering WIP

Agglomerative and divisive approaches to hierarchical clustering

5.

K-Means Clustering

Iterative centroid-based clustering with elbow method for optimal K

6.

Linear Discriminant Analysis

Supervised dimensionality reduction maximizing class separation via Fisher's criterion

7.

Principal Component Analysis

Unsupervised dimensionality reduction via eigenvectors of the covariance matrix