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A Taxonomy of Clutter Reduction for Information Visualisation

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A Taxonomy of Clutter Reduction for Information Visualisation. / Ellis, G.; Dix, Alan.

In: IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 6, 2007, p. 1216-1223.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Ellis, G & Dix, A 2007, 'A Taxonomy of Clutter Reduction for Information Visualisation', IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1216-1223. https://doi.org/10.1109/TVCG.2007.70535

APA

Ellis, G., & Dix, A. (2007). A Taxonomy of Clutter Reduction for Information Visualisation. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1216-1223. https://doi.org/10.1109/TVCG.2007.70535

Vancouver

Ellis G, Dix A. A Taxonomy of Clutter Reduction for Information Visualisation. IEEE Transactions on Visualization and Computer Graphics. 2007;13(6):1216-1223. https://doi.org/10.1109/TVCG.2007.70535

Author

Ellis, G. ; Dix, Alan. / A Taxonomy of Clutter Reduction for Information Visualisation. In: IEEE Transactions on Visualization and Computer Graphics. 2007 ; Vol. 13, No. 6. pp. 1216-1223.

Bibtex

@article{13055813c95046dea69165dce664c646,
title = "A Taxonomy of Clutter Reduction for Information Visualisation",
abstract = "Information visualisation is about gaining insight into data through a visual representation. This data is often multivariate and increasingly, the datasets are very large. To help us explore all this data, numerous visualisation applications, both commercial and research prototypes, have been designed using a variety of techniques and algorithms. Whether they are dedicated to geo-spatial data or skewed hierarchical data, most of the visualisations need to adopt strategies for dealing with overcrowded displays, brought about by too much data to fit in too small a display space. This paper analyses a large number of these clutter reduction methods, classifying them both in terms of how they deal with clutter reduction and more importantly, in terms of the benefits and losses. The aim of the resulting taxonomy is to act as a guide to match techniques to problems where different criteria may have different importance, and more importantly as a means to critique and hence develop existing and new techniques.",
keywords = "cs_eprint_id, 1434 cs_uid, 373",
author = "G. Ellis and Alan Dix",
year = "2007",
doi = "10.1109/TVCG.2007.70535",
language = "English",
volume = "13",
pages = "1216--1223",
journal = "IEEE Transactions on Visualization and Computer Graphics",
issn = "1077-2626",
publisher = "IEEE Computer Society",
number = "6",

}

RIS

TY - JOUR

T1 - A Taxonomy of Clutter Reduction for Information Visualisation

AU - Ellis, G.

AU - Dix, Alan

PY - 2007

Y1 - 2007

N2 - Information visualisation is about gaining insight into data through a visual representation. This data is often multivariate and increasingly, the datasets are very large. To help us explore all this data, numerous visualisation applications, both commercial and research prototypes, have been designed using a variety of techniques and algorithms. Whether they are dedicated to geo-spatial data or skewed hierarchical data, most of the visualisations need to adopt strategies for dealing with overcrowded displays, brought about by too much data to fit in too small a display space. This paper analyses a large number of these clutter reduction methods, classifying them both in terms of how they deal with clutter reduction and more importantly, in terms of the benefits and losses. The aim of the resulting taxonomy is to act as a guide to match techniques to problems where different criteria may have different importance, and more importantly as a means to critique and hence develop existing and new techniques.

AB - Information visualisation is about gaining insight into data through a visual representation. This data is often multivariate and increasingly, the datasets are very large. To help us explore all this data, numerous visualisation applications, both commercial and research prototypes, have been designed using a variety of techniques and algorithms. Whether they are dedicated to geo-spatial data or skewed hierarchical data, most of the visualisations need to adopt strategies for dealing with overcrowded displays, brought about by too much data to fit in too small a display space. This paper analyses a large number of these clutter reduction methods, classifying them both in terms of how they deal with clutter reduction and more importantly, in terms of the benefits and losses. The aim of the resulting taxonomy is to act as a guide to match techniques to problems where different criteria may have different importance, and more importantly as a means to critique and hence develop existing and new techniques.

KW - cs_eprint_id

KW - 1434 cs_uid

KW - 373

U2 - 10.1109/TVCG.2007.70535

DO - 10.1109/TVCG.2007.70535

M3 - Journal article

VL - 13

SP - 1216

EP - 1223

JO - IEEE Transactions on Visualization and Computer Graphics

JF - IEEE Transactions on Visualization and Computer Graphics

SN - 1077-2626

IS - 6

ER -