Inference of hidden structures in complex physical systems by multi-scale clustering
Abstract
We survey the application of a relatively new branch of statistical physics--"community detection"-- to data mining. In particular, we focus on the diagnosis of materials and automated image segmentation. Community detection describes the quest of partitioning a complex system involving many elements into optimally decoupled subsets or communities of such elements. We review a multiresolution variant which is used to ascertain structures at different spatial and temporal scales. Significant patterns are obtained by examining the correlations between different independent solvers. Similar to other combinatorial optimization problems in the NP complexity class, community detection exhibits several phases. Typically, illuminating orders are revealed by choosing parameters that lead to extremal information theory correlations.
Keywords
Cite
@article{arxiv.1503.01626,
title = {Inference of hidden structures in complex physical systems by multi-scale clustering},
author = {Z. Nussinov and P. Ronhovde and Dandan Hu and S. Chakrabarty and M. Sahu and Bo Sun and N. A. Mauro and K. K. Sahu},
journal= {arXiv preprint arXiv:1503.01626},
year = {2017}
}
Comments
25 pages, 16 Figures; a review of earlier works