论文标题
评论“改进的共同信息量度用于聚类，分类和社区检测”
Comment on "Improved mutual information measure for clustering, classification, and community detection"
论文作者
论文摘要
最近的一篇文章提出，减少了共同信息，以评估聚类，分类和社区检测。动机是，标准的归一化互信息（NMI）可以在某些条件下，尤其是在考虑的两个部门之间的群集数不同时给出违反直觉的答案。动机是有道理的。但是，本文中给出的示例不准确，此评论讨论了原因。此外，此评论还从经验上表明，减少的共同信息无法应付NMI的困难，甚至无法带来更多。在此评论中，Kappa的必要性也得到了经验验证。
A recent article proposed reduced mutual information for evaluation of clustering, classification and community detection. The motivation is that the standard normalized mutual information (NMI) may give counter-intuitive answers under certain conditions and particularly when the number of clusters differs between the two divisions under consideration. The motivation makes sense. However, the examples given in the article are not accurate, and this comment discusses why. In addition, this comment also empirically demonstrates that the reduced mutual information cannot handle the difficulties of NMI and even brings more. The necessity of Kappa is also empirically validated in this comment.
