Clustering without labels

Unsupervised learning is useful when you do not already have labels but still need to organize messy records. Clustering groups items that look similar to the system. It does not prove the group name is true.

For customer feedback, clustering can help a team see possible themes: billing confusion, login problems, feature requests. A human still needs to inspect examples, name the theme, and split mixed clusters.

The builder artifact is a cluster summary with sample records, a proposed name, and a review flag.