The growing hierarchical self-organizing maps: exploratory analysis of high-dimensional data

@misc{GHSOM, 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 related, on the one hand, to the static architecture of this model, as well as, on the other hand, to the limited capabilities for the representation of hierarchical relations of the data. With our novel Growing Hierarchical SelfOrganizing Map presented in this paper we address...}, author = {Rauber, A. and Merkl, D. and Dittenbach, M. }, citeulike-article-id = {1972782}, keywords = {ghsom, som}, posted-at = {2008-06-24 17:49:20}, priority = {0}, title = {The growing hierarchical self-organizing maps: exploratory analysis of high-dimensional data}, url = {http://citeseer.ist.psu.edu/569850.html}, year = {2002} }

TY - GEN ID - GHSOM L3 - citeulike-article-id:1972782 TI - The growing hierarchical self-organizing maps: exploratory analysis of high-dimensional data N2 - 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 related, on the one hand, to the static architecture of this model, as well as, on the other hand, to the limited capabilities for the representation of hierarchical relations of the data. With our novel Growing Hierarchical SelfOrganizing Map presented in this paper we address... KW - ghsom KW - som AU - Rauber, A AU - Merkl, D AU - Dittenbach, M PY - 2002/// UR - http://citeseer.ist.psu.edu/569850.html ER -