Constraint-Based Clustering Selection
release_6ixyemragfcp5gu4zbnol7omuy
by
Toon Van Craenendonck, Hendrik Blockeel
2016
Abstract
Semi-supervised clustering methods incorporate a limited amount of
supervision into the clustering process. Typically, this supervision is
provided by the user in the form of pairwise constraints. Existing methods use
such constraints in one of the following ways: they adapt their clustering
procedure, their similarity metric, or both. All of these approaches operate
within the scope of individual clustering algorithms. In contrast, we propose
to use constraints to choose between clusterings generated by very different
unsupervised clustering algorithms, run with different parameter settings. We
empirically show that this simple approach often outperforms existing
semi-supervised clustering methods.
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