A New Non-archimedean Metric on Persistent Homology
This work offers a new metric for persistent homology, potentially improving clustering and analysis for researchers working with topological data analysis.
This paper introduces a new non-archimedean metric, the cophenetic metric, for persistent homology classes of all degrees. Experimental results on various datasets show that zeroth persistent homology with this metric and hierarchical clustering provides statistically verifiable topological information, leading to clusters with strong silhouette scores and Rand indices.
In this article, we define a new non-archimedean metric structure, called cophenetic metric, on persistent homology classes of 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 different 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.