We discuss an approach to data clustering. We find that maximum likelihood
leads naturally to an Hamiltonian of Potts variables that depends on the co
rrelation matrix and whose low temperature behavior describes the correlati
on structure of the data. For random, uncorrelated data sets no correlation
structure emerges. On the other hand, for data sets with a built-in cluste
r structure, the method is able to detect and recover efficiently that stru
cture. Finally we apply the method to financial time series, where the low-
temperature behavior reveals a nontrivial clustering.