Ranked Centroid Projection: A Data Visualization Approach With Self-Organizing Maps

@article{citeulike:2975440, abstract = {The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e., document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text mining. The proposed approach first transforms the document space into a multidimensional vector space by means of document encoding. Afterwards, a growing hierarchical SOM (GHSOM) is trained and used as a baseline structure to automatically produce maps with various levels of detail. Following the GHSOM training, the new projection method, namely the ranked centroid projection (RCP), is applied to project the input vectors to a hierarchy of 2D output maps. The RCP is used as a data analysis tool as well as a direct interface to the data. In a set of simulations, the proposed approach is applied to an illustrative data set and two real-world scientific document collections to demonstrate its applicability.}, author = {Yen, G. G. and Wu, Zheng }, booktitle = {Neural Networks, IEEE Transactions on}, citeulike-article-id = {2975440}, doi = {http://dx.doi.org/10.1109/TNN.2007.905858}, journal = {Neural Networks, IEEE Transactions on}, keywords = {som}, number = {2}, pages = {245--259}, posted-at = {2008-07-09 09:39:42}, priority = {2}, title = {Ranked Centroid Projection: A Data Visualization Approach With Self-Organizing Maps}, url = {http://dx.doi.org/10.1109/TNN.2007.905858}, volume = {19}, year = {2008} }

TY - JOUR ID - citeulike:2975440 L3 - citeulike-article-id:2975440 TI - Ranked Centroid Projection: A Data Visualization Approach With Self-Organizing Maps T2 - Neural Networks, IEEE Transactions on JF - Neural Networks, IEEE Transactions on VL - 19 IS - 2 SP - 245 EP - 259 N2 - The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e., document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text mining. The proposed approach first transforms the document space into a multidimensional vector space by means of document encoding. Afterwards, a growing hierarchical SOM (GHSOM) is trained and used as a baseline structure to automatically produce maps with various levels of detail. Following the GHSOM training, the new projection method, namely the ranked centroid projection (RCP), is applied to project the input vectors to a hierarchy of 2D output maps. The RCP is used as a data analysis tool as well as a direct interface to the data. In a set of simulations, the proposed approach is applied to an illustrative data set and two real-world scientific document collections to demonstrate its applicability. KW - som AU - Yen, GG AU - Wu, Zheng PY - 2008/// UR - http://dx.doi.org/10.1109/TNN.2007.905858 ER -