复杂物理系统隐结构的多尺度聚类推断
Inference of hidden structures in complex physical systems by multi-scale clustering
摘要：
我们调查的一个相对较新的分支统计物理学-“社区检测”-数据挖掘的应用。特别是，我们专注于材料的诊断和自动图像分割。社区检测描述了将包含许多元素的复杂系统划分为这些元素的最佳解耦子集或社区的探索。我们回顾了一个多分辨率的变体，它是用来确定在不同的空间和时间尺度的结构。通过检查不同的独立求解器之间的相关性，获得显着的模式。与NP复杂性类中的其他组合优化问题类似，社区检测也有几个阶段。通常情况下，照明秩序揭示了选择参数，导致极端的信息理论的相关性。
英文摘要：
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.
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