Topological analysis of data
Abstract 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,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2017-06-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1140/epjds/s13688-017-0104-x |
| _version_ | 1818526962751111168 |
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| author | Alice Patania Francesco Vaccarino Giovanni Petri |
| author_facet | Alice Patania Francesco Vaccarino Giovanni Petri |
| author_sort | Alice Patania |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-11T06:30:00Z |
| format | Article |
| id | doaj.art-d6c8458c046943dd8f11bc5cc3253493 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-11T06:30:00Z |
| publishDate | 2017-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-d6c8458c046943dd8f11bc5cc32534932022-12-22T01:17:33ZengSpringerOpenEPJ Data Science2193-11272017-06-01611610.1140/epjds/s13688-017-0104-xTopological analysis of dataAlice Patania0Francesco Vaccarino1Giovanni Petri2ISI FoundationISI FoundationISI FoundationAbstract 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.http://link.springer.com/article/10.1140/epjds/s13688-017-0104-xtopological data analysissimplicial complexespersistent homology |
| spellingShingle | Alice Patania Francesco Vaccarino Giovanni Petri Topological analysis of data EPJ Data Science topological data analysis simplicial complexes persistent homology |
| title | Topological analysis of data |
| title_full | Topological analysis of data |
| title_fullStr | Topological analysis of data |
| title_full_unstemmed | Topological analysis of data |
| title_short | Topological analysis of data |
| title_sort | topological analysis of data |
| topic | topological data analysis simplicial complexes persistent homology |
| url | http://link.springer.com/article/10.1140/epjds/s13688-017-0104-x |
| work_keys_str_mv | AT alicepatania topologicalanalysisofdata AT francescovaccarino topologicalanalysisofdata AT giovannipetri topologicalanalysisofdata |