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11th International Conference Information Visualization (IV '07)
2D and 3D Neural-Network Based Visualization of High-Dimensional Biomedical Data
Zurich, Switzerland
July 04-July 06
ISBN: 0-7695-2900-3
ASCII Text
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Urska Cvek, Marjan Trutschl, John C. Cannon, Rona S. Scott, Robert E. Rhoads, "2D and 3D Neural-Network Based Visualization of High-Dimensional Biomedical Data," 2010 14th International Conference Information Visualisation, pp. 545-550, 11th International Conference Information Visualization (IV '07), 2007.
 
 
BibTex
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@article{ 10.1109/IV.2007.5,
author = {Urska Cvek and Marjan Trutschl and John C. Cannon and Rona S. Scott and Robert E. Rhoads},
title = {2D and 3D Neural-Network Based Visualization of High-Dimensional Biomedical Data},
journal ={2010 14th International Conference Information Visualisation},
volume = {0},
year = {2007},
issn = {1550-6037},
pages = {545-550},
doi = {http://doi.ieeecomputersociety.org/10.1109/IV.2007.5},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - 2010 14th International Conference Information Visualisation
TI - 2D and 3D Neural-Network Based Visualization of High-Dimensional Biomedical Data
SN - 1550-6037
SP545
EP550
A1 - Urska Cvek,
A1 - Marjan Trutschl,
A1 - John C. Cannon,
A1 - Rona S. Scott,
A1 - Robert E. Rhoads,
PY - 2007
KW - null
VL - 0
JA - 2010 14th International Conference Information Visualisation
ER -
 
Urska Cvek, Louisiana State University, Shreveport, LA
Marjan Trutschl, Louisiana State University, Shreveport, LA
John C. Cannon, Louisiana State University, Shreveport, LA
Rona S. Scott, LSUHSC, Shreveport, LA
Robert E. Rhoads, LSUHSC, Shreveport, LA
In this paper we integrate self-organizing map algorithm (SOM) with scatter plot and Radviz, extending these visualizations into the third dimension and reducing overlap. Classic visualizations are used as the twodimensional base, combined with a self-organizing map that extends them into the third dimension, with an adjusted neighborhood function. This approach solves the problem of overlap where more than one point plots to the same space and uncovers additional information about relationships inherent in high-dimensional data sets, including distribution of points, outliers and associations. Case studies are presented on a microarray and miRNA data sets.
Citation:
Urska Cvek, Marjan Trutschl, John C. Cannon, Rona S. Scott, Robert E. Rhoads, "2D and 3D Neural-Network Based Visualization of High-Dimensional Biomedical Data," iv, pp.545-550, 11th International Conference Information Visualization (IV '07), 2007
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