Constraint-Based Clustering Selection

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 procedu...

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Bibliographic Details
Published in:arXiv.org 2016-09
Main Authors: Toon Van Craenendonck, Blockeel, Hendrik
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Online Access:Get full text
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Summary: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.
ISSN:2331-8422