Name: Clustering Algorithms Lab
Author: MySimulator

Algorithm

Dataset shape

k (clusters): 3

Iteration0

Inertia (SSE)—

ε neighbourhood radius: 0.6 Min points: 4

Clusters found—

Noise points—

K-means needs you to pick k in advance and always finds spherical clusters. DBSCAN infers cluster count from density and can find odd shapes — but relies on ε and min-points instead.

Cluster assignment