Unsupervised Learning: Implementation & Application

Unsupervised learning seeks structure in data without ordinary target labels.

K-means alternates assignments and centroid updates to reduce within-cluster squared distance.

Internal scores, stability under resampling, visualization, external labels when available only for evaluation, and domain interpretation help assess whether discovered structure is useful.