Name
Title | ||
|---|---|---|
Organizing and Exploring High-Dimensional Data with the Growing Hierarchical Self-Organizing Map |
Abstract | ||
|---|---|---|
The Self-Organizing Map is a very popular unsupervised neural network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are caused, on the one hand, by the static architecture of this model, as well as, on the other hand, by the limited capabilities for the representation of hierarchical relations of the data. With our Growing Hierarchical Self-Organizing Mapwe present an artificial neural network model with hierarchical archi- tecture composed of independent growing self-organizing maps to address both limitations. The motivation is to pro- vide a model that adapts its architecture during its unsuper- vised training process according to the particular require- ments of the input data. The benefits of this neural network are first, a problem-dependent architecture, and second, the intuitive representation of hierarchical relations in the data. This is especially appealing in exploratory data mining ap- plications, allowing the inherent structure of the data to un- fold in a highly intuitive fashion. |
Year | Venue | Keywords |
|---|---|---|
2002 | FSKD | data mining,artificial neural network,high dimensional data,self organization,neural network |
Field | DocType | Citations |
Artificial neural network model,Architecture,Clustering high-dimensional data,Computer science,Self-organizing map,Artificial intelligence,Artificial neural network,Machine learning | Conference | 8 |
PageRank | References | Authors |
0.65 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Dittenbach | 1 | 297 | 26.48 |
| Dieter Merkl | 2 | 846 | 115.65 |
| Andreas Rauber | 3 | 1925 | 216.21 |