Topological and Statistical Methods for Complex Data: Tackling Large-Scale, High-Dimensional, and Multivariate Data Spaces: Mathematics and Visualization
Editat de Janine Bennett, Fabien Vivodtzev, Valerio Pascuccien Limba Engleză Hardback – 3 dec 2014
The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends.
Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.
Toate formatele și edițiile | Preț | Express |
---|---|---|
Paperback (1) | 1086.37 lei 43-57 zile | |
Springer Berlin, Heidelberg – 20 aug 2016 | 1086.37 lei 43-57 zile | |
Hardback (1) | 1092.23 lei 43-57 zile | |
Springer Berlin, Heidelberg – 3 dec 2014 | 1092.23 lei 43-57 zile |
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Specificații
ISBN-13: 9783662448991
ISBN-10: 3662448998
Pagini: 311
Ilustrații: XV, 297 p. 120 illus., 101 illus. in color.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.62 kg
Ediția:2015
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Mathematics and Visualization
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3662448998
Pagini: 311
Ilustrații: XV, 297 p. 120 illus., 101 illus. in color.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.62 kg
Ediția:2015
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Mathematics and Visualization
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
I. Large-scale data analysis: In-situ and distributed analysis.- II. Large-scale data analysis: Efficient representation of large-functions.- III. Multi-variate data analysis: Structural techniques.- IV. Multi-variate data analysis: Classification and visualization of vector fields.- V. High-dimensional data analysis: Exploration of high-dimensional models.- VI. High-dimensional data analysis: Analysis of large systems.
Textul de pe ultima copertă
This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the field who examine challenges as well as detail solutions to the analysis of extreme scale data.
The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends.
Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.
The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends.
Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.
Caracteristici
Latest peer-reviewed results in a growing research area Many applications in science and engineering Important contributions to the fields of mathematics and computer science? Includes supplementary material: sn.pub/extras