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Topological Methods in Data Analysis and Visualization V: Theory, Algorithms, and Applications

Persistence -- Scalar Topology -- Time-Varying Topology -- Multivariate Topology -- Other Forms of Topology.

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Bibliographic Details
Other Authors: Carr, Hamish (Editor) , Fujishiro, Issei (Editor) , Sadlo, Filip (Editor) , Takahashi, Shigeo (Editor)
Format: Conference Paper
Language:English
Published: Cham Springer International Publishing 2020.
Cham Imprint: Springer 2020.
Edition:1st ed. 2020.
Series:Mathematics and Visualization
Springer eBook Collection
DOI:10.1007/978-3-030-43036-8
Online Access:Resolving-System, lizenzpflichtig: https://doi.org/10.1007/978-3-030-43036-8
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Author Notes:edited by Hamish Carr, Issei Fujishiro, Filip Sadlo, Shigeo Takahashi

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520 |a This collection of peer-reviewed workshop papers provides comprehensive coverage of cutting-edge research into topological approaches to data analysis and visualization. It encompasses the full range of new algorithms and insights, including fast homology computation, comparative analysis of simplification techniques, and key applications in materials and medical science. The book also addresses core research challenges such as the representation of large and complex datasets, and integrating numerical methods with robust combinatorial algorithms. In keeping with the focus of the TopoInVis 2017 Workshop, the contributions reflect the latest advances in finding experimental solutions to open problems in the sector. They provide an essential snapshot of state-of-the-art research, helping researchers to keep abreast of the latest developments and providing a basis for future work. Gathering papers by some of the world’s leading experts on topological techniques, the book represents a valuable contribution to a field of growing importance, with applications in disciplines ranging from engineering to medicine. 
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