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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

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.

Item URL in elib:https://elib.dlr.de/131977/
Document Type:Conference or Workshop Item (Speech)
Title:Statistical Parameter Selection for Clustering Persistence Diagrams
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Kontak, MaxMax.Kontak (at) DLR.dehttps://orcid.org/0000-0003-3738-7483
Vidal, JulesSorbonne UniversiteUNSPECIFIED
Tierny, JulienSorbonne UniversiteUNSPECIFIED
Date:2019
Journal or Publication Title:2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1109/UrgentHPC49580.2019.00007
Page Range:pp. 7-12
Status:Published
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
Organizer:IEEE/ACM
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben SISTEC
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:09 Dec 2019 11:42

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