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

@article{citeulike:2939462, abstract = {The self-organizing map (SOM) 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 to the static architecture of this model as well as to the limited capabilities for the representation of hierarchical relations of the data. With our novel growing hierarchical SOM (GHSOM) we address both limitations. The GHSOM is an artificial neural-network model with hierarchical architecture composed of independent growing SOMs. The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. Furthermore, by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated. The benefits of this novel neural network are a problem-dependent architecture and the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.}, author = {Rauber, A. and Merkl, D. and Dittenbach, M. }, booktitle = {Neural Networks, IEEE Transactions on}, citeulike-article-id = {2939462}, doi = {http://dx.doi.org/10.1109/TNN.2002.804221}, journal = {Neural Networks, IEEE Transactions on}, keywords = {som, textmining}, number = {6}, pages = {1331--1341}, posted-at = {2008-06-28 18:10:33}, priority = {4}, title = {The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data}, url = {http://dx.doi.org/10.1109/TNN.2002.804221}, volume = {13}, year = {2002} }

TY - JOUR ID - citeulike:2939462 L3 - citeulike-article-id:2939462 N2 - The self-organizing map (SOM) 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 to the static architecture of this model as well as to the limited capabilities for the representation of hierarchical relations of the data. With our novel growing hierarchical SOM (GHSOM) we address both limitations. The GHSOM is an artificial neural-network model with hierarchical architecture composed of independent growing SOMs. The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. Furthermore, by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated. The benefits of this novel neural network are a problem-dependent architecture and the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion. IS - 6 JF - Neural Networks, IEEE Transactions on EP - 1341 TI - The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data VL - 13 T2 - Neural Networks, IEEE Transactions on SP - 1331 KW - som KW - textmining AU - Rauber, A AU - Merkl, D AU - Dittenbach, M PY - 2002/// UR - http://dx.doi.org/10.1109/TNN.2002.804221 ER -