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

Mesoscopic analysis of networks: applications to exploratory analysis and data clustering