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Conference Paper

Consistent Minimization of Clustering Objective Functions

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons76237

von Luxburg,  U
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83837

Bubeck,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83994

Jegelka,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

von Luxburg, U., Bubeck, S., Jegelka, S., & Kaufmann, M. (2008). Consistent Minimization of Clustering Objective Functions. Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007, 961-968.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C735-4
Abstract
Clustering is often formulated as a discrete optimization problem. The objective is to find, among all partitions of the data set, the best one according to some quality measure. However, in the statistical setting where we assume that the finite data set has been sampled from some underlying space, the goal is not to find the best partition of the given sample, but to approximate the true partition of the underlying space. We argue that the discrete optimization approach usually does not achieve this goal. As an alternative, we suggest the paradigm of nearest neighbor clusteringamp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lsquo;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lsquo;. Instead of selecting the best out of all partitions of the sample, it only considers partitions in some restricted function class. Using tools from statistical learning theory we prove that nearest neighbor clustering is statistically consistent. Moreover, its worst case complexity is polynomial by co nstructi on, and it can b e implem ented wi th small average case co mplexity using b ranch an d bound.