K-Means
DBSCAN
This tool visualizes different clustering algorithms side-by-side on the same dataset.
K-Means: Partitions data into a user-defined number of spherical clusters (K). It's fast but struggles with complex shapes and noise.
DBSCAN: Groups points based on density. It excels at finding arbitrarily shaped clusters and identifying outliers as noise.
Agglomerative: A bottom-up hierarchical approach that merges the closest pair of clusters at each step until a target number of clusters is reached.
Gaussian Mixture (GMM): A probabilistic model assuming data is from several Gaussian distributions. It can identify elliptical clusters.