Grouping related examples, particularly during unsupervised learning. Once all the examples are grouped, a human can optionally supply meaning to each cluster.
Many clustering algorithms exist. For example, the k-means algorithm clusters examples based on their proximity to a centroid, as in the following diagram:
A human researcher could then review the clusters and, for example, label cluster 1 as “dwarf trees” and cluster 2 as “full-size trees.”
As another example, consider a clustering algorithm based on an example’s distance from a center point, illustrated as follows: