[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar, PAA 1409 (UdeM), Fri Sept 28, 11:30am

Hugo Larochelle larocheh at IRO.UMontreal.CA
Mon Sep 24 17:29:04 EDT 2007


This week's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en):


How Many Clusters? An Information-Theoretic Perspective


by Susanna Still,
Department of Information and Computer Sciences
University of Hawaii

Location: Pavillon Andre-Aisenstadt (UdeM), room 1409
Time: September 28th 2007, 11h30

Clustering provides a common means of identifying structure in complex data, and there is renewed interest in clustering as a tool for the analysis of large data sets in many fields.Anatural question is how many clusters are appropriate for the description of a given system. Traditional approaches to this problem are based on either a framework in which clusters of a particular shape are assumed as a model of the system or on a two-step procedure in which a clustering criterion determines the optimal assignments for a given number of clusters and a separate criterion measures the goodness of the classification to determine the number of clusters. In a statistical mechanics approach, clustering can be seen as a trade-off between energy- and entropy-like terms, with lower temperature driving the proliferation of clusters to provide a more detailed description of the data. For finite data sets, we expect that there is a limit to the meaningful structure that can be resolved and therefore a minimum temperature beyond which we will capture sampling noise. This suggests that correcting the clustering criterion for the bias that arises due to sampling errors will allow us to find a clustering solution at a temperature that is optimal in the sense that we capture maximal meaningful structure-without having to define an external criterion for the goodness or stability of the clustering. We show that in a general information-theoretic framework, the finite size of a data set determines an optimal temperature, and we introduce a method for finding the maximal number of clusters that can be resolved from the data in the hard clustering limit.




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