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Springer Topological Methods in Data Analysis and Visualization V

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Description

Explore the latest research in topological approaches to data analysis and visualization with this peer-reviewed collection from Springer. This volume brings together workshop papers that cover a wide range of new algorithms and theoretical insights. Readers can examine advancements in fast homology computation and comparative studies of simplification techniques. This book addresses the fundamental challenges of modern data science, such as representing large, complex datasets and combining numerical methods with robust combinatorial algorithms. It provides practical perspectives through key applications in fields like medical science and materials science. Whether you are looking for theoretical foundations or algorithmic applications, this text offers a deep look into the evolving landscape of mathematics and visualization.

Key Features

Covers cutting-edge research regarding topological approaches for data analysis and visualization.

Provides insights into new algorithms including fast homology computation techniques.

Includes comparative analysis of various simplification techniques used in data science.

Explores real-world applications in medical science and materials science research.

Addresses methods for the representation of large and complex datasets.

Discusses the integration of numerical methods with robust combinatorial algorithms.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
12 December 2021
Listed Since
14 November 2021

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