  The correlation function of galaxy clusters has often been used as a test of
cosmological models. A number of assumptions are implicit in the comparison of
theoretical expectations to data. Here we use an ensemble of ten large N-body
simulations of the standard cold dark matter cosmology to investigate how
cluster selection criteria and other uncertain factors influence the cluster
correlation function. We consider the effects of varying the definition of a
cluster, the mean number density (or equivalently the richness) in a catalogue,
and the assumed normalisation of the model; we also examine the importance of
redshift space distortions. We implement five different group-finding
algorithms and construct cluster catalogues defined by mass, velocity
dispersion or a measure of X-ray luminosity. We find that different cluster
catalogues yield correlation functions which can differ by substantially more
than the statistical errors in any one determination. For a given cluster
selection criteria, the correlation length typically varies by $\sim 20\%$ in
catalogues spanning the range of intercluster separations covered by the APM
and Abell (richness class $\gsim 1$) catalogues. Distortions produced by
peculiar velocities in redshift space enhance the correlation function at large
separations and lead to a larger clustering length in redshift space than in
real space. The sensitivity of the cluster correlation function to various
uncertain model assumptions substantially weakens previous conclusions based on
the comparison of model predictions with real data. Detailed modelling of
cluster selection procedures including the effects of selecting from projected
galaxy catalogues is required before the cluster correlation function can be
regarded as a high precision constraint on cosmological models.
