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http://hdl.handle.net/1942/33757
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DC Field | Value | Language |
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dc.contributor.author | Alcaide, Daniel | - |
dc.contributor.author | AERTS, Jan | - |
dc.date.accessioned | 2021-03-29T07:34:12Z | - |
dc.date.available | 2021-03-29T07:34:12Z | - |
dc.date.issued | 2020 | - |
dc.date.submitted | 2021-03-29T07:14:10Z | - |
dc.identifier.citation | IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 27 (10), p. 3994-4008. | - |
dc.identifier.issn | 1077-2626 | - |
dc.identifier.issn | 1941-0506 | - |
dc.identifier.issn | 2160-9306 | - |
dc.identifier.uri | http://hdl.handle.net/1942/33757 | - |
dc.description.abstract | The connections in a graph generate a structure that is independent of a coordinate system. This visual metaphor allows creating a more flexible representation of data than a two-dimensional scatterplot. In this work, we present STAD (Simplified Topological Abstraction of Data), a parameter-free dimensionality reduction method that projects high-dimensional data into a graph. STAD generates an abstract representation of high-dimensional data by giving each data point a location in a graph which preserves the approximate distances in the original high-dimensional space. The STAD graph is built upon the Minimum Spanning Tree (MST) to which new edges are added until the correlation between the distances from the graph and the original dataset is maximized. Additionally, STAD supports the inclusion of additional functions to focus the exploration and allow the analysis of data from new perspectives, emphasizing traits in data which otherwise would remain hidden. We demonstrate the effectiveness of our method by applying it to two real-world datasets: traffic density in Barcelona and temporal measurements of air quality in Castile and Leon in ´ Spain. | - |
dc.description.sponsorship | The authors wish to thank Danai Kafetzaki for valuable feedback and proofreading. This work was supported in part by the IWT/SBO 150056 project ”ACquiring CrUcial Medical information Using LAnguage TEchnology” (ACCUMULATE), and by the Flanders AI Impulse Program (“Onderzoeksprogramma Artificiele Intelligentie (AI) ¨ Vlaanderen”). | - |
dc.language.iso | en | - |
dc.publisher | - | |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. | - |
dc.subject.other | Visual analytics | - |
dc.subject.other | Networks | - |
dc.subject.other | Dimensionality reduction | - |
dc.subject.other | Data transformation. | - |
dc.title | Spanning Trees as Approximation of Data Structures | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 4008 | - |
dc.identifier.issue | 10 | - |
dc.identifier.spage | 3994 | - |
dc.identifier.volume | 27 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1109/tvcg.2020.2995465 | - |
dc.identifier.isi | 000692890200012 | - |
dc.identifier.eissn | 1941-0506 | - |
local.provider.type | CrossRef | - |
local.uhasselt.uhpub | yes | - |
local.uhasselt.international | no | - |
item.fullcitation | Alcaide, Daniel & AERTS, Jan (2020) Spanning Trees as Approximation of Data Structures. In: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 27 (10), p. 3994-4008.. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2022 | - |
item.contributor | Alcaide, Daniel | - |
item.contributor | AERTS, Jan | - |
item.accessRights | Open Access | - |
crisitem.journal.issn | 1077-2626 | - |
crisitem.journal.eissn | 1941-0506 | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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Spanning_Trees_as_Approximation_of_Data_Structures.pdf | Published version | 16.18 MB | Adobe PDF | View/Open |
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