.normalized mutual information计算公式normalized mutual information 归一化互信息(Normalized Mutual Information,NMI)是一种用于度量两个数据集聚类结果之间的相似性的指标。它是基于互信息(Mutual Information,MI)的度量,但通过对聚类结果的规模进行归一化,以便于不同规模的聚类结果之间的比较。 NMI的计算公式如下所示:...
在数据科学和机器学习的领域,评估和比较不同分组或聚类的质量至关重要。其中,互信息(Mutual Information, MI)是一种衡量两个变量之间依赖关系的指标。为了更好地比较不同大小的数据集,通常使用归一化互信息(Normalized Mutual Information, NMI)。本文将通过Python代码示例,帮助你理解NMI的概念及其应用。 什么是互信息?
实验室最近用到nmi( Normalized Mutual information )评价聚类效果,在网上找了一下这个算法的实现,发现满意的不多. 浙江大学蔡登教授有一个,http://www.zjucadcg.cn/dengcai/Data/code/MutualInfo.m ,他在数据挖掘届地位很高,他实现这个算法的那篇论文引用率高达三位数。但这个实现,恕个人能力有限,我实在是没有...
information gain ratio也与NMI有些类似。 I(A,B)=H(A)+H(B)-H(A,B)>=0,而且I(A,B)的最大值在A,B完全一样时取到,此时H(A|B)=0,所以NMI(A,B)=2*H(A)/(H(A)+H(B))=1,所以NMI(A,B)在[0,1]范围。 http://blog.sina.com.cn/s/blog_45e6be080101dlya.html...
2) normal mutual information (NMI) 归一化互信息(NMI) 3) normalized mutual information entropy 归一化互信息熵 4) normalized mutual information vector 归一化互信息向量 1. Multi-image registration based on entropy of normalized mutual information vector 基于归一化互信息向量熵的多幅图像配准方法 ...
The Normalized Mutual Information (NMI) metric is widely utilized in the evaluation of clustering and community detection algorithms. This study explores the performance of NMI, specifically examining its performance in relation to the quantity of commun
还有个python的版本http://blog.sun.tc/2010/10/mutual-informationmi-and-normalized-mutual-informationnmi-for-numpy.html,这个感觉很靠谱,作者对nmi的理解和我是一样的。 我的理解来自wiki和stanford,其实很简单,先说一下问题:例如stanford中介绍的一个例子: ...
还有个python的版本http://blog./2010/10/mutual-informationmi-and-normalized-mutual-informationnmi-for-numpy.html,这个感觉很靠谱,作者对nmi的理解和我是一样的。 我的理解来自wiki和stanford,其实很简单,先说一下问题:例如stanford中介绍的一个例子: ...
normalized mutual informationNormalized mutual information (NMI) is a widely used measure to compare community detection methods. Recently, however, the need of adjustment for information theory-based measures has been argued because of the so-called selection bias problem, that is, they show the ...
normalized mutual informationA new estimation of distribution algorithm based on normalized mutual information (NMIEDA) is proposed for overcoming the premature convergence of bivariate estimation of distribution algorithms. NMIEDA first uses normalized mutual information to measure the interaction between two...