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

Ronald Peikert, Helwig Hauser, Hamish Carr, Raphael Fuchs

BOOK, 2012

Abstract

When scientists analyze datasets in a search for underlying phenomena, patterns or causal factors, their first step is often an automatic or semi-automatic search for structures in the data. Of these feature-extraction methods, topological ones stand out due to their solid mathematical foundation. Topologically defined structures -as found in scalar, vector and tensor fields- have proven their merit in a wide range of scientific domains, and scientists have found them to be revealing in subjects such as physics, engineering, and medicine. Full of state-of-the-art research and contemporary hot topics in the subject, this volume is a selection of peer-reviewed papers originally presented at the fourth Workshop on Topology-Based Methods in Data Analysis and Visualization, TopoInVis 2011, held in Zurich, Switzerland. The workshop brought together many of the leading lights in the field for a mixture of formal presentations and discussion. One topic currently generating a great deal of interest, and explored in several chapters here, is the search for topological structures in time-dependent flows, and their relationship with Lagrangian coherent structures. Contributors also focus on discrete topologies of scalar and vector fields, and on persistence-based simplification, among other issues of note. The new research results included in this volume relate to all three key areas in data analysis´┐Żtheory, algorithms and applications.

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BibTeX

@book{peikert12topological,
  author = {Ronald Peikert and Helwig Hauser and Hamish Carr and Raphael Fuchs},
  title = {Topological Methods in Data Analysis and Visualization II: Theory, 
    Algorithms, and Applications},
  year = {2012}, 
  series = {Mathematics and Visualization},
  abstract = {
   When scientists analyze datasets in a search for underlying phenomena, patterns 
   or causal factors, their first step is often an automatic or semi-automatic search 
   for structures in the data. Of these feature-extraction methods, topological ones 
   stand out due to their solid mathematical foundation. Topologically defined 
   structures -as found in scalar, vector and tensor fields- have proven their merit 
   in a wide range of scientific domains, and scientists have found them to be 
   revealing in subjects such as physics, engineering, and medicine.
   Full of state-of-the-art research and contemporary hot topics in the subject, this
   volume is a selection of peer-reviewed papers originally presented at the fourth 
   Workshop on Topology-Based Methods in Data Analysis and Visualization, TopoInVis 
   2011, held in Zurich, Switzerland. The workshop brought together many of the leading 
   lights in the field for a mixture of formal presentations and discussion. One topic 
   currently generating a great deal of interest, and explored in several chapters here, 
   is the search for topological structures in time-dependent flows, and their relationship 
   with Lagrangian coherent structures. Contributors also focus on discrete topologies 
   of scalar and vector fields, and on persistence-based simplification, among other 
   issues of note. The new research results included in this volume relate to all 
   three key areas in data analysis´┐Żtheory, algorithms and applications.},
  publisher = {Springer},


  isbn = {978-3-642-23175-9},
  URL = {http://www.springer.com/mathematics/computational+science+%26+engineering/book/978-3-642-23174-2},
}






 Last Modified: Jean-Paul Balabanian, 2013-11-18