The stability of a good clustering

author: Marina Meila, Department of Statistics, University of Washington
published: Feb. 25, 2007,   recorded: August 2006,   views: 5985


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If we have found a "good" clustering C of data set X, can we prove that C is not far from the (unknown) best clustering C* of this data set? Perhaps surprisingly, the answer to this question is sometimes yes. We can show bounds on the distance( C, C* ) for two clustering cost functions: the Normalized Cut and the squared distance cost of K-means clustering. These bounds exist in the case when the data X admits a "good" clustering for the given cost.

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