Constrained clustering is a semisupervised approach to clustering data while incorporating domain knowledge in the form of constraints. The constraints are usually expressed as pairwise statements indicating that two items must, or cannot, be placed into the same cluster. Constrained clustering algorithms may enforce every constraint in the solution, or they may use the constraints as guidance rather than hard requirements.
Motivation and Background
Unsupervised learningoperates without any domain-specific guidance or preexisting knowledge. Supervised learning requires that all training examples be associated with labels. Yet it is often the case that existing knowledge for a problem domain fits neither of these extremes. Semisupervised learning methods fill this gap by making use of both labeled and unlabeled data. Constrained clustering, a form of semisupervised learning, was developed to extend clustering algorithms to incorporate existing domain knowledge, when...
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