An EM-Approach for clustering multi-instance objects

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

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Abstract

In many data mining applications the data objects are modeled as sets of feature vectors or multi-instance objects. In this paper, we present an expectation maximization approach for clustering multi-instance objects. We therefore present a statistical process that models multi-instance objects. Furthermore, we present M-steps and E-steps for EM clustering and a method for finding a good initial model. In our experimental evaluation, we demonstrate that the new EM algorithm is capable to increase the cluster quality for three real world data sets compared to a k-medoid clustering.