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Lecture Notes in Computer Science
Constrained clustering is a recently presented family of semisupervised learning algorithms. These methods use domain information to impose constraints over the clustering output. The way in which those constraints (typically pair-wise constraints between documents) are introduced is by designing new clustering algorithms that enforce the accomplishment of the constraints. In this paper we present an alternative approach for constrained clustering where, instead of defining new algorithms ordoi:10.1007/978-3-642-28997-2_30 fatcat:nh3xkas2gfe6dlgdpgql4jzgfa