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Statistical Parameter Selection for Clustering Persistence Diagrams

Kontak, Max and Vidal, Jules and Tierny, Julien (2019) Statistical Parameter Selection for Clustering Persistence Diagrams. In: 2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC), pp. 7-12. 2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC), 16. Nov. - 22. Nov. 2019, Denver, USA. doi: 10.1109/UrgentHPC49580.2019.00007.

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Official URL: https://conferences.computer.org/sc19w/2019/#!/toc/21


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.

Item URL in elib:https://elib.dlr.de/131977/
Document Type:Conference or Workshop Item (Speech)
Title:Statistical Parameter Selection for Clustering Persistence Diagrams
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kontak, MaxUNSPECIFIEDhttps://orcid.org/0000-0003-3738-7483UNSPECIFIED
Vidal, JulesSorbonne UniversiteUNSPECIFIEDUNSPECIFIED
Tierny, JulienSorbonne UniversiteUNSPECIFIEDUNSPECIFIED
Journal or Publication Title:2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 7-12
Keywords:urgent decision making, ensemble simulation, topological clustering, statistical model selection
Event Title:2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC)
Event Location:Denver, USA
Event Type:Workshop
Event Dates:16. Nov. - 22. Nov. 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben SISTEC (old)
Location: Köln-Porz
Institutes and Institutions:Institut of Simulation and Software Technology > High Performance Computing
Institut of Simulation and Software Technology
Deposited By: Kontak, Max
Deposited On:09 Dec 2019 11:42
Last Modified:20 Jun 2021 15:53

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