Clustering

Also written: clusters, k-means

Grouping similar examples together without being told the groups in advance.

Other terms from the same lesson

• elbow method: Picking the number of clusters where adding more stops making the clusters much tighter: the bend in the curve.
• inertia: How tight a k-means clustering is: the total squared distance from each point to its cluster's center. Lower is tighter.
• silhouette score: How well each point sits in its own cluster rather than the next one: near 1 is clear-cut, near 0 is on a border.