Machine Learning Fundamentals · Unsupervised learning

K-means clustering

Grouping unlabeled points into k clusters by repeatedly assigning them to the nearest center and recomputing centers.

Also known as: k-means, centroid clustering.

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Learn first

• Types of ML problems: The basic split between supervised (labeled examples), unsupervised (no labels), and reinforcement learning.

Leads to

• Hierarchical clustering: Building a tree of nested clusters by repeatedly merging (or splitting) groups, instead of picking a fixed number upfront.

Related topics

• Hierarchical clustering: Building a tree of nested clusters by repeatedly merging (or splitting) groups, instead of picking a fixed number upfront.