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100   $a20141119d2015      u|             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aTopological and Statistical Methods for Complex Data$b[electronic resource] $eTackling Large-Scale, High-Dimensional, and Multivariate Data Spaces /$fedited by Janine Bennett, Fabien Vivodtzev, Valerio Pascucci
205   $a1st ed. 2015.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2015.
215   $a1 online resource (297 p.)
225 1 $aMathematics and Visualization,$x1612-3786
300   $a"With 120 Figures, 101 in color."
311   $a3-662-44899-8 
320   $aIncludes bibliographical references at the end of each chapters and index.
327   $aI. 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.
330   $aThis book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the field who examine challenges as well as detail solutions to the analysis of extreme scale data.   The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends.   Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.
410  0$aMathematics and Visualization,$x1612-3786
606   $aTopology
606   $aStatistics 
606   $aApplied mathematics
606   $aEngineering mathematics
606   $aAlgorithms
606   $aMathematics
606   $aVisualization
606   $aManifolds (Mathematics)
606   $aComplex manifolds
606   $aTopology$3https://scigraph.springernature.com/ontologies/product-market-codes/M28000
606   $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001
606   $aApplications of Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M13003
606   $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018
606   $aVisualization$3https://scigraph.springernature.com/ontologies/product-market-codes/M14034
606   $aManifolds and Cell Complexes (incl. Diff.Topology)$3https://scigraph.springernature.com/ontologies/product-market-codes/M28027
615  0$aTopology.
615  0$aStatistics .
615  0$aApplied mathematics.
615  0$aEngineering mathematics.
615  0$aAlgorithms.
615  0$aMathematics.
615  0$aVisualization.
615  0$aManifolds (Mathematics).
615  0$aComplex manifolds.
615 14$aTopology.
615 24$aStatistical Theory and Methods.
615 24$aApplications of Mathematics.
615 24$aAlgorithms.
615 24$aVisualization.
615 24$aManifolds and Cell Complexes (incl. Diff.Topology).
676   $a514
702   $aBennett$b Janine$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aVivodtzev$b Fabien$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aPascucci$b Valerio$4edt$4http://id.loc.gov/vocabulary/relators/edt
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910299775103321
996   $aTopological and Statistical Methods for Complex Data$91564826
997   $aUNINA