  We begin by reviewing some probabilistic results about the Dirichlet Process
and its close relatives, focussing on their implications for statistical
modelling and analysis. We then introduce a class of simple mixture models in
which clusters are of different `colours', with statistical characteristics
that are constant within colours, but different between colours. Thus cluster
identities are exchangeable only within colours. The basic form of our model is
a variant on the familiar Dirichlet process, and we find that much of the
standard modelling and computational machinery associated with the Dirichlet
process may be readily adapted to our generalisation. The methodology is
illustrated with an application to the partially-parametric clustering of gene
expression profiles.

Methodology;Computation