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- S. Seo, J. Mohr, and
K. Obermayer. A new incremental pairwise clustering algorithm.
.
In Proceedings of the ICMLA -09: The Eighth International Conference on
Machine Learning and Applications, Los Alamitos, CA, USA, 2009. IEEE
Computer Society.
(accepted).
Pairwise clustering methods are able to handle relational data, in
which a set of objects is described via a matrix of pairwise
(dis)similarities. Here, we consider a cost function for pairwise clustering
which maximizes model entropy under the constraint that the error for
reconstructing objects from class information is fixed to a small value.
Based on the analysis of structural transitions, we derive a new incremental
pairwise clustering method which increases the number of clusters until a
certain value of a Lagrange multiplier is reached. In addition, the
calculation of phase transitions is used for speed-up. The incremental
duplication of clusters helps to avoid local optima, and the stopping
criterion automatically determines the number of clusters. The performance of
the method is assessed on artificial and real-world data.
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