Topological analysis of data

Propelled by a fast evolving landscape of techniques and datasets, data science is growing rapidly. Against this background, topological data analysis (TDA) has carved itself a niche for the analysis of datasets that present complex interactions and rich structures. Its distinctive feature, topology...

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Bibliographic Details
Published inEPJ data science Vol. 6; no. 1; pp. 7 - 6
Main Authors Patania, Alice, Vaccarino, Francesco, Petri, Giovanni
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2017
Springer Nature B.V
SpringerOpen
Subjects
Commentary
Complexity
Computer Appl. in Social and Behavioral Sciences
Computer Science
Data analysis
Data science
Data-driven Science
Datasets
Modeling and Theory Building
persistent homology
simplicial complexes
topological data analysis
Topology
simplicial complexes
topological data analysis
persistent homology
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Summary:Propelled by a fast evolving landscape of techniques and datasets, data science is growing rapidly. Against this background, topological data analysis (TDA) has carved itself a niche for the analysis of datasets that present complex interactions and rich structures. Its distinctive feature, topology, allows TDA to detect, quantify and compare the mesoscopic structures of data, while also providing a language able to encode interactions beyond networks. Here we briefly present the TDA paradigm and some applications, in order to highlight its relevance to the data science community.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Correspondence-1
content type line 14
ISSN:2193-1127
2193-1127
DOI:10.1140/epjds/s13688-017-0104-x