The stability of a good clustering

نویسنده

  • Marina Meilă
چکیده

If we have found a ”good” clustering C of a data set, can we prove that C is not far from the (unknown) best clustering C of these data? Perhaps surprisingly, the answer to this question is sometimes yes. This paper proves spectral bounds on the distance d(C, C) for the case when “goodness” is measured by a quadratic cost, such as the squared distortion of K-means clustering, or the Normalized Cut criterion of spectral clustering. The bounds exist if the data admits a “good”, low-cost clustering.

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تاریخ انتشار 2006