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Mesoscopic analysis of networks: Applications to exploratory analysis and data clustering
25. See http://deim.urv.cat/sgomez/radatools.php Toolbox for community detection.
31. R. M. Durbin, “Studies on the Development and Organisation of the Nervous System of Caenorhabditis elegans,” Ph.D. thesis (University of Cambridge, 1987).
32. G. Gan, C. Ma, and J. Wu, Data Clustering: Theory, Algorithms, and Applications, ASA-SIAM Series on Statistics and Applied Probability, 20 (SIAM Philadelphia ASA, Alexandria, VA, 2007).
34. R. A. Fisher, Ann. Eugen. 7, 179 (1936).
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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 structure of correlation matrices. Using a multiresolution 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 Caenorhabditis elegans and to automatically classify a typical benchmark of unsupervised clustering, the Iris dataset, with considerable success.
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