Name
Abstract | ||
|---|---|---|
The Self-Organizing Map is a popular neural network model for data analysis, for which a wide variety of visualization techniques ex- ists. We present two novel techniques that take the density of the data into account. Our methods deflne graphs resulting from nearest neighbor- and radius-based distance calculations in data space and show projections of these graph structures on the map. It can then be observed how relations between the data are preserved by the projection, yielding interesting in- sights into the topology of the mapping, and helping to identify outliers as well as dense regions. |
Year | Venue | Keywords |
|---|---|---|
2005 | ESANN | neural network model,data analysis,nearest neighbor |
Field | DocType | Citations |
k-nearest neighbors algorithm,Graph,Pattern recognition,Computer science,Outlier,Sight,Self-organizing map,Nearest neighbor graph,Artificial intelligence,Artificial neural network,Machine learning,Creative visualization | Conference | 6 |
PageRank | References | Authors |
0.57 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georg Pölzlbauer | 1 | 57 | 4.84 |
| Andreas Rauber | 2 | 1925 | 216.21 |
| Michael Dittenbach | 3 | 297 | 26.48 |