  We give the motivation for scoring clustering algorithms and a metric $M : A
\rightarrow \mathbb{N}$ from the set of clustering algorithms to the natural
numbers which we realize as \begin{equation} M(A) = \sum_i \alpha_i |f_i -
\beta_i|^{w_i} \end{equation} where $\alpha_i,\beta_i,w_i$ are parameters used
for scoring the feature $f_i$, which is computed empirically.. We give a method
by which one can score features such as stability, noise sensitivity, etc and
derive the necessary parameters. We conclude by giving a sample set of scores.
