In computer science, constrained clustering is a class of semi-supervised learning algorithms.
A cluster in which the members conform to all must-link and cannot-link constraints is called a chunklet.
Both a must-link and a cannot-link constraint define a relationship between two data instances.
Together, the sets of these constraints act as a guide for which a constrained clustering algorithm will attempt to find chunklets (clusters in the dataset which satisfy the specified constraints).
Constraints could also be used to guide the selection of a clustering model among several possible solutions.