Visual exploration of large data sets
Communications of the ACM
A survey of visualizations for high-dimensional data mining
Information visualization in data mining and knowledge discovery
Self-Organizing Maps
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Intelligent data analysis
An accurate MDS-Based algorithm for the visualization of large multidimensional datasets
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
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In this paper, the relative multidimensional scaling method is investigated. This method is designated to visualize large multidimensional data. The method encompasses application of multidimensional scaling (MDS) to the so-called basic vector set and further mapping of the remaining vectors from the analyzed data set. In the original algorithm of relative MDS, the visualization process is divided into three steps: the set of basis vectors is constructed using the k-means clustering method; this set is projected onto the plane using the MDS algorithm; the set of remaining data is visualized using the relative mapping algorithm. We propose a modification, which differs from the original algorithm in the strategy of selecting the basis vectors. The experimental investigation has shown that the modification exceeds the original algorithm in the visualization quality and computational expenses. The conditions, where the relative MDS efficiency exceeds that of standard MDS, are estimated.