Consistency  of spectral clustering

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Consistency of spectral clustering (2004)

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by Ulrike von Luxburg , Mikhail Belkin , Olivier Bousquet

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570 - 15 self

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@INPROCEEDINGS{Luxburg04consistencyof,
    author = {Ulrike von Luxburg and Mikhail Belkin and Olivier Bousquet},
    title = {Consistency of spectral clustering},
    booktitle = {},
    year = {2004},
    pages = {857--864},
    publisher = {MIT Press}
}

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Abstract

Consistency is a key property of statistical algorithms, when the data is drawn from some underlying probability distribution. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms. In this paper we investigate consistency of a popular family of spectral clustering algorithms, which cluster the data with the help of eigenvectors of graph Laplacian matrices. We show that one of the two of major classes of spectral clustering (normalized clustering) converges under some very general conditions, while the other (unnormalized), is only consistent under strong additional assumptions, which, as we demonstrate, are not always satisfied in real data. We conclude that our analysis provides strong evidence for the superiority of normalized spectral clustering in practical applications. We believe that methods used in our analysis will provide a basis for future exploration of Laplacian-based methods in a statistical setting.

Keyphrases

spectral clustering major class strong additional assumption popular family practical application statistical setting real data normalized clustering normalized spectral clustering strong evidence graph laplacian matrix general condition future exploration laplacian-based method statistical algorithm key property underlying probability distribution

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