Normalized Mutual Information(NMI, 归一化互信息) 值域是 \([0,1]\),值越高表示两个聚类结果越相似。归一化是指将两个聚类结果的相似性值定量到 \(0\sim 1\) 之间。 \[\text{NMI}=\frac{2\sum_i\sum_jn_{ij}ln\
.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:...
Normalized mutual information is often used for evaluating clustering results, information retrieval, feature selection etc. This is a optimized implementation of the function which has no for loops. This function is now a part of the PRML toolbox (http://www.mathworks.com/matlabcentral/file...
NMI的英文全称是Normalized Mutual Information,中文叫做标准化互信息,它可以用来衡量两种聚类结果的相似度。 本文介绍适用于重叠聚类的NMI计算步骤,重叠指的是,一个节点可以属于多个类别。 假设一个图中的真实社团如下所示: 1 2 3 4 3 4 5 6 7 6 7 8 9 1 2 3 1 2 3 4 3 4 5 6 7 6 7 8 9 ...
Normalized Mutual Information归一化互信息 其中I(A,B)是A,B两向量的mutual information, H(A)是A向量的信息熵。 I(A,B)=H(A)-H(A|B)=H(B)-H(B|A),这也好理解,直觉上,如果已知B的情况,A的条件熵H(B|A)相对于H(A)变小了,即不确定程度变小,那么B能提供对A有用的信息,越有用,越相近。互...
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 ...
print("normalized mutual information (using mutual_info_regression):", nmi)在上述代码中，首先使用 ...
python normalized mutual information 深入理解Python中的归一化互信息 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)。
Hurley, "Normalized Mutual Information to evaluate overlapping community finding algorithms", CoRR (2011) abs/1110.2515McDaid A F, Greene D and Hurley N 2011 Normalized mutual information to evaluate overlapping community finding algorithms ArXiv Prepr. ArXiv11102515...
% Normalized Mutual information Hx =0; % Entropies foridA = A_ids idAOccurCount = length( find( A == idA ) ); Hx = Hx - (idAOccurCount/total) * log2(idAOccurCount/total + eps); end Hy =0; % Entropies foridB = B_ids ...