Clustering multi-represented objects using combination trees

Authors:
Elke Achtert;Hans-Peter Kriegel;Alexey Pryakhin;Matthias Schubert
Affiliations:
Institute for Computer Science, University of Munich, Germany;Institute for Computer Science, University of Munich, Germany;Institute for Computer Science, University of Munich, Germany;Institute for Computer Science, University of Munich, Germany
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

Citing 3
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Abstract

When clustering complex objects, there often exist various feature transformations and thus multiple object representations. To cluster multi-represented objects, dedicated data mining algorithms have been shown to achieve improved results. In this paper, we will introduce combination trees for describing arbitrary semantic relationships which can be used to extend the hierarchical clustering algorithm OPTICS to handle multi-represented data objects. To back up the usability of our proposed method, we present encouraging results on real world data sets.