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A New Non-archimedan Metric on Persistent Homology

Computational statistics (Zeitschrift) (CSZ), 2020
Abstract

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.

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