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Title: Computing and visualizing time-varying merge trees for high-dimensional data

Abstract

We introduce a new method that identifies and tracks features in arbitrary dimensions using the merge tree -- a structure for identifying topological features based on thresholding in scalar fields. This method analyzes the evolution of features of the function by tracking changes in the merge tree and relates features by matching subtrees between consecutive time steps. Using the time-varying merge tree, we present a structural visualization of the changing function that illustrates both features and their temporal evolution. We demonstrate the utility of our approach by applying it to temporal cluster analysis of high-dimensional point clouds.

Authors:
 [1];  [2];  [3];  [3];  [1]
  1. Univ. of Leipzig (Germany)
  2. Univ. of Kaiserslautern (Germany)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1393102
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Book
Resource Relation:
Related Information: Book Title: Mathematics and Visualization
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Oesterling, Patrick, Heine, Christian, Weber, Gunther H., Morozov, Dmitry, and Scheuermann, Gerik. Computing and visualizing time-varying merge trees for high-dimensional data. United States: N. p., 2017. Web. doi:10.1007/978-3-319-44684-4_5.
Oesterling, Patrick, Heine, Christian, Weber, Gunther H., Morozov, Dmitry, & Scheuermann, Gerik. Computing and visualizing time-varying merge trees for high-dimensional data. United States. doi:10.1007/978-3-319-44684-4_5.
Oesterling, Patrick, Heine, Christian, Weber, Gunther H., Morozov, Dmitry, and Scheuermann, Gerik. Sat . "Computing and visualizing time-varying merge trees for high-dimensional data". United States. doi:10.1007/978-3-319-44684-4_5. https://www.osti.gov/servlets/purl/1393102.
@article{osti_1393102,
title = {Computing and visualizing time-varying merge trees for high-dimensional data},
author = {Oesterling, Patrick and Heine, Christian and Weber, Gunther H. and Morozov, Dmitry and Scheuermann, Gerik},
abstractNote = {We introduce a new method that identifies and tracks features in arbitrary dimensions using the merge tree -- a structure for identifying topological features based on thresholding in scalar fields. This method analyzes the evolution of features of the function by tracking changes in the merge tree and relates features by matching subtrees between consecutive time steps. Using the time-varying merge tree, we present a structural visualization of the changing function that illustrates both features and their temporal evolution. We demonstrate the utility of our approach by applying it to temporal cluster analysis of high-dimensional point clouds.},
doi = {10.1007/978-3-319-44684-4_5},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {6}
}

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