Name
Title | ||
|---|---|---|
Uncovering hierarchical structure in data using the growing hierarchical self-organizing map |
Abstract | ||
|---|---|---|
Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In particular, the representation of hierarchical relations and intuitively visible cluster boundaries are essential for a wide range of data mining applications. Current approaches based on neural networks hardly fulfill these requirements within a single model. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1016/S0925-2312(01)00655-5 | Neurocomputing |
Keywords | Field | DocType |
Self-organizing map (SOM),Unsupervised hierarchical clustering,Document classification,Data mining,Exploratory data analysis | Document classification,Data mining,Data collection,Architecture,Feature vector,Pattern recognition,Computer science,Self-organizing map,Artificial intelligence,Artificial neural network,Exploratory data analysis,Machine learning | Journal |
Volume | Issue | ISSN |
48 | 1 | 0925-2312 |
Citations | PageRank | References |
55 | 3.55 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Dittenbach | 1 | 297 | 26.48 |
| Andreas Rauber | 2 | 1925 | 216.21 |
| Dieter Merkl | 3 | 846 | 115.65 |