computer science Computer science is the study of computation, information, and automation. Computer science spans Theoretical computer science, theoretical disciplines (such as algorithms, theory of computation, and information theory) to Applied science, ...

, constrained clustering is a class of

semi-supervised learning Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them. It is charact ...

algorithms. Typically, constrained clustering incorporates either a set of must-link constraints, cannot-link constraints, or both, with a

data clustering Cluster analysis or clustering is the data analyzing technique in which task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each o ...

algorithm. A cluster in which the members conform to all must-link and cannot-link constraints is called a chunklet.

Types of constraints

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). * A ''must-link constraint'' is used to specify that the two instances in the must-link relation should be associated with the same cluster. * A ''cannot-link constraint'' is used to specify that the two instances in the cannot-link relation should ''not'' be associated with the same cluster. Some constrained clustering algorithms will abort if no such clustering exists which satisfies the specified constraints. Others will try to minimize the amount of constraint violation should it be impossible to find a clustering which satisfies the constraints. Constraints could also be used to guide the selection of a clustering model among several possible solutions.

Examples

Examples of constrained clustering algorithms include: * COP K-means * PCKmeans (Pairwise Constrained K-means) * CMWK-Means (Constrained Minkowski Weighted K-Means)

References

Cluster analysis algorithms Cluster analysis {{machine-learning-stub