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Statistical Parameter Selection for Clustering Persistence Diagrams

Abstract : In urgent decision making applications, ensemble simulations are an important way to determine different outcome scenarios based on currently available data. In this paper, we will analyze the output of ensemble simulations by considering so-called persistence diagrams, which are reduced representations of the original data, motivated by the extraction of topological features. Based on a recently published progressive algorithm for the clustering of persistence diagrams, we determine the optimal number of clusters, and therefore the number of significantly different outcome scenarios, by the minimization of established statistical score functions. Furthermore, we present a proof-of-concept prototype implementation of the statistical selection of the number of clusters and provide the results of an experimental study, where this implementation has been applied to real-world ensemble data sets.
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Submitted on : Monday, October 21, 2019 - 3:18:30 PM
Last modification on : Sunday, June 26, 2022 - 2:41:40 AM
Long-term archiving on: : Wednesday, January 22, 2020 - 4:32:53 PM


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  • HAL Id : hal-02321869, version 1


Max Kontak, Jules Vidal, Julien Tierny. Statistical Parameter Selection for Clustering Persistence Diagrams. SuperComputing Workshop on UrgentHPC, Nov 2019, Denver, United States. ⟨hal-02321869⟩



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