Embedded Map Projection for Dimensionality Reduction-Based Similarity Search
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Nonlinear Embedded Map Projection for Dimensionality Reduction
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Adaptive FIR neural model for centroid learning in self-organizing maps
IEEE Transactions on Neural Networks
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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.