  We show that model-based Bayesian clustering, the probabilistically most
systematic approach to the partitioning of data, can be mapped into a
statistical physics problem for a gas of particles, and as a result becomes
amenable to a detailed quantitative analysis. A central role in the resulting
statistical physics framework is played by an entropy function. We demonstrate
that there is a relevant parameter regime where mean-field analysis of this
function is exact, and that, under natural assumptions, the lowest entropy
state of the hypothetical gas corresponds to the optimal clustering of data.
The byproduct of our analysis is a simple but effective clustering algorithm,
which infers both the most plausible number of clusters in the data and the
corresponding partitions. Describing Bayesian clustering in statistical
mechanical terms is found to be natural and surprisingly effective.
