MIC：基于互信息的层次聚类
MIC: Mutual Information based hierarchical Clustering
摘要：
集群是一个在各种各样的应用程序中使用的概念。我们回顾了一个概念上非常简单的层次聚类算法，在下面的{ \it互信息聚类}（MIC）算法。它使用互信息（MI）作为相似性度量，并利用其分组属性：三个对象X，Y和Z之间的MI等于X和Y之间的MI之和，加上Z和组合对象（XY）之间的MI。我们在信息论的香农（概率）版本中使用MIC，其中“对象”是由随机样本表示的概率分布，以及在柯尔莫哥洛夫（算法）版本中，其中“对象”是符号序列。我们应用我们的方法从线粒体DNA序列的系统发育树的建设，我们重建胎儿心电图的输出的独立成分分析（ICA）适用于孕妇的心电图。
英文摘要：
Clustering is a concept used in a huge variety of applications. We review a conceptually very simple algorithm for hierarchical clustering called in the following the {\it mutual information clustering} (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X, Y, and Z is equal to the sum of the MI between X and Y, plus the MI between Z and the combined object (XY). We use MIC both in the Shannon (probabilistic) version of information theory, where the "objects" are probability distributions represented by random samples, and in the Kolmogorov (algorithmic) version, where the "objects" are symbol sequences. We apply our method to the construction of phylogenetic trees from mitochondrial DNA sequences and we reconstruct the fetal ECG from the output of independent components analysis (ICA) applied to the ECG of a pregnant woman.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
