Title: Generalizing from example clusters
Authors: Hu, Pan
Vens, Celine
Verstrynge, Bart
Blockeel, Hendrik
Issue Date: Oct-2013
Publisher: Springer
Host Document: Lecture Notes in Computer Science vol:8140 pages:64-78
Conference: Discovery Science edition:16 location:Singapore date:6-9 October 2013
Abstract: We consider the following problem: Given a set of data and one or
more examples of clusters, find a clustering of the whole data set that is consistent with the given clusters. This is essentially a semi-supervised clustering problem, but it differs from previously studied semi-supervised clustering settings in significant ways. Earlier work has shown that none of the existing methods for semi-supervised clustering handle this problem well.We identify two reasons for this, which are related to the default metric learning methods not working well in this situation, and to overfitting behavior. We investigate the latter in more detail and propose a new method that explicitly guards against overfitting. Experimental results confirm that the new method generalizes much better. Several other problems identified here remain open.
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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