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Clustering small datasets in high-dimension by random projection
The different structures uncovered are found to persist as more points are added to the dataset.
A highly likely clusterable data model with no clusters
We propose a model for a dataset in ${\mathbb R}^D$ that does not contain any clusters but yet is such that a projection of the points on a random one-dimensional subspace is likely to yield a clustering of the points.
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