  We investigate the adaptation and performance of modularity-based algorithms,
designed in the scope of complex networks, to analyze the mesoscopic structure
of correlation matrices. Using a multi-resolution analysis we are able to
describe the structure of the data in terms of clusters at different
topological levels. We demonstrate the applicability of our findings in two
different scenarios: to analyze the neural connectivity of the nematode {\em
Caenorhabditis elegans}, and to automatically classify a typical benchmark of
unsupervised clustering, the Iris data set, with considerable success.
