  Using the random-cluster representation of the $q$-state Potts models we
consider the pooling of data from cluster-update Monte Carlo simulations for
different thermal couplings $K$ and number of states per spin $q$. Proper
combination of histograms allows for the evaluation of thermal averages in a
broad range of $K$ and $q$ values, including non-integer values of $q$. Due to
restrictions in the sampling process proper normalization of the combined
histogram data is non-trivial. We discuss the different possibilities and
analyze their respective ranges of applicability.
