This paper describes a class of models we call semi-supervised clustering. Algorithms in this category are clustering methods that use information possessed by labeled training data X~d < R~p as well as structural information that resides in the unlabeled data X~u > R~p. The labels are used in conjunction with the unlabeled data to help clustering Algorithms partition X~u > R~p which then terminate without the capability to label Other points in. This is very different from supervised learning, wherein the training Data subsequently endow a classifier with the ability to label every point in R~p.
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