ITEM METADATA RECORD
Title: Constraint-based clustering selection
Authors: Van Craenendonck, Toon
Blockeel, Hendrik
Issue Date: Sep-2016
Host Document: Benelearn 2016 Poster Presentations
Conference: Benelearn location:Kortrijk date:2016
Article number: 21
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.
Publication status: accepted
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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