Unsupervised Learning

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

1.

Principal Component Analysis

Unsupervised dimensionality reduction via eigenvectors of the covariance matrix

2.

K-Means Clustering

Iterative centroid-based clustering with elbow method for optimal K

3.

Hierarchical Clustering WIP

Agglomerative and divisive approaches to hierarchical clustering

4.

DBSCAN

Density-based clustering for arbitrary cluster shapes

5.

HDBSCAN

Hierarchical density-based clustering that handles varying densities

6.

Gaussian Mixture Models

Soft clustering using EM algorithm with Gaussian distributions

7.

Linear Discriminant Analysis

Supervised dimensionality reduction maximizing class separation via Fisher's criterion