Self-Organizing Maps
Graph drawing by stress majorization
GD'04 Proceedings of the 12th international conference on Graph Drawing
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data
Similarity-Based Clustering
Software Cartography: thematic software visualization with consistent layout
Journal of Software Maintenance and Evolution: Research and Practice - Working Conference on Reverse Engineering (WCRE 2008)
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
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Multidimensional Scaling (MDS) is a powerful dimension reduction technique for embedding high-dimensional data into a low-dimensional target space. Thereby, the distance relationships in the source are reconstructed in the target space as best as possible according to a given embedding criterion. Here, a new stress function with intuitive properties and a very good convergence behavior is presented. Optimization is combined with an efficient implementation for calculating dynamic distance matrix correlations, and the implementation can be transferred to other related algorithms. The suitability of the proposed MDS for high-throughput data (HiT-MDS) is studied in applications to macroarray analysis for up to 12,000 genes.