In this article, we define a new non-archimedian metric structure, called cophenetic metric, on persistent homology classes for all degrees. We then show that zeroth persistent homology together with the cophenetic metric and hierarchical clustering algorithms with a number of different metrics do deliver statistically verifiable commensurate topological information based on experimental results we obtained on different datasets. We also observe that the resulting clusters coming from cophenetic distance do shine in terms of internal and external evaluation measures such as silhouette score and the Rand index. Moreover, since the cophenetic metric is defined for all homology degrees, one can now display the inter-relations of persistent homology classes in all degrees via rooted trees.
翻译:在本篇文章中,我们定义了一个新的非结构性指标结构,称为同系同系物指数,用于所有学位的持久性同系物类别。然后我们表明,零持久性同系物以及带有若干不同指标的同系物指标和等级组合算法确实根据我们从不同数据集获得的实验结果,提供了统计上可核实的相称的地貌信息。我们还注意到,从同系物距离产生的组群在内部和外部评估措施方面,例如硅色分和Rand指数方面,确实闪烁光芒。此外,由于同系物指标是为所有同系物学位定义的,因此现在人们可以通过根树显示不同程度的同系物类别之间的相互关系。