This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
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
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
Usage of this product signifies your acceptance of the Terms of Use.