A. Ben-Hur, A. Elisseeff and I. Guyon.
Pacific Symposium on Biocomputing, pp. 6-17
2002
We present a method for visually and quantitatively assessing the
presence of structure in clustered data. The method exploits measurements
of the stability of clustering solutions obtained by perturbing the data
set. Stability is characterized by the distribution of pairwise similarities
between clusterings obtained from sub-samples of the data. High pairwise
similarities indicate a stable clustering pattern. The method can be used
with any clustering algorithm; it provides a means of rationally defining
an optimum number of clusters, and can also detect the lack of structure
in data. We show results on artificial and microarray data using a hierarchical
clustering algorithm.
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