Don't trust a mathematical measure. Analyze your data manually.
Otherwise, you risk "overfitting", in the sense that the clustering algorithm appears to be best that happens to be best correlated to your evaluation method. This happens all the time; people comparing k-means and other clusters based on the in-cluster variance. Now k-means tries to minimize in-cluster variance... so it is not surprising that it scores well on this measure. But you could say that this measure mostly tests how similar your clustering to a k-means clustering is.
Some of these measures have a well-defined use case. Assuming you run k-means multiple times (which is a best practice), which result should you choose? When comparing k-means with k-means, it is definitely okay to use such a measure. It will tell you which of the two the better k-means result is.
As for "fpc", it probably refers to the R clustering package "fpc". I believe it also includes some cluster validation indexes.