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Abstract:
In this paper, we investigate stability-based methods
for cluster model selection, in particular to select
the number K of clusters. The scenario under
consideration is that clustering is performed
by minimizing a certain clustering quality function,
and that a unique global minimizer exists. On
the one hand we show that stability can be upper
bounded by certain properties of the optimal clustering,
namely by the mass in a small tube around
the cluster boundaries. On the other hand, we provide
counterexamples which show that a reverse
statement is not true in general. Finally, we give
some examples and arguments why, from a theoretic
point of view, using clustering stability in a
high sample setting can be problematic. It can be
seen that distribution-free guarantees bounding the
difference between the finite sample stability and
the true stability cannot exist, unless one makes
strong assumptions on the underlying distribution.
