An overview of mapping techniques for exploratory pattern analysis
Pattern Recognition
Experiments on mapping techniques for exploratory pattern analysis
Pattern Recognition
Self-organizing maps
Non-linear dimensionality reduction techniques for unsupervised feature extraction
Pattern Recognition Letters
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Self-organizing feature maps with self-adjusting learning parameters
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
Robust analysis of MRS brain tumour data using t-GTM
Neurocomputing
Class proximity measures - Dissimilarity-based classification and display of high-dimensional data
Journal of Biomedical Informatics
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We introduce a distance (similarity)--based mapping for the visualization of high-dimensional patterns and their relative relationships. The mapping preserves exactly the original distances between points with respect to any two reference patterns in a special two-dimensional coordinate system, the relative distance plane (RDP). As only a single calculation of a distance matrix is required, this method is computationally efficient, an essential requirement for any exploratory data analysis. The data visualization afforded by this representation permits a rapid assessment of class pattern distributions. In particular, we can determine with a simple statistical test whether both training and validation sets of a 2-class, high-dimensional dataset derive from the same class distributions. We can explore any dataset in detail by identifying the subset of reference pairs whose members belong to different classes, cycling through this subset, and for each pair, mapping the remaining patterns. These multiple viewpoints facilitate the identification and confirmation of outliers. We demonstrate the effectiveness of this method on several complex biomedical datasets. Because of its efficiency, effectiveness, and versatility, one may use the RDP representation as an initial, data mining exploration that precedes classification by some classifier. Once final enhancements to the RDP mapping software are completed, we plan to make it freely available to researchers.