  We discuss a new approach to data clustering. We find that maximum likelyhood
leads naturally to an Hamiltonian of Potts variables which depends on the
correlation matrix and whose low temperature behavior describes the correlation
structure of the data. For random, uncorrelated data sets no correlation
structure emerges. On the other hand for data sets with a built-in cluster
structure, the method is able to detect and recover efficiently that structure.
Finally we apply the method to financial time series, where the low temperature
behavior reveals a non trivial clustering.
