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The stability of a good clustering
This video was recorded at Machine Learning Summer School (MLSS), Taipei 2006. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
