  Model-based clustering is a powerful tool that is often used to discover
hidden structure in data by grouping observational units that exhibit similar
response values. Recently, clustering methods have been developed that permit
incorporating an ``initial'' partition informed by expert opinion. Then, using
some similarity criteria, partitions different from the initial one are down
weighted, i.e. they are assigned reduced probabilities. These methods represent
an exciting new direction of method development in clustering techniques. We
add to this literature a method that very flexibly permits assigning varying
levels of uncertainty to any subset of the partition. This is particularly
useful in practice as there is rarely clear prior information with regards to
the entire partition. Our approach is not based on partition penalties but
considers individual allocation probabilities for each unit (e.g., locally
weighted prior information). We illustrate the gains in prior specification
flexibility via simulation studies and an application to a dataset concerning
spatio-temporal evolution of ${\rm PM}_{10}$ measurements in Germany.
