SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A linear iteration time layout algorithm for visualising high-dimensional data
Proceedings of the 7th conference on Visualization '96
Fast multidimensional scaling through sampling, springs and interpolation
Information Visualization
Steerable, Progressive Multidimensional Scaling
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Improving Angle Based Mappings
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Visualization-Driven Structural and Statistical Analysis of Turbulent Flows
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
AVEDA: Statistical Tests for Finding Interesting Visualisations
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Landscape multidimensional scaling
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Case-Centred multidimensional scaling for classification visualisation in medical diagnosis
HIS'13 Proceedings of the second international conference on Health Information Science
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Many applications in science and business such as signal analysis or costumer segmentation deal with large amounts of data which are usually high dimensional in the feature space. As a part of preprocessing and exploratory data analysis, visualization of the data helps to decide which kind of method probably leads to good results. Since the visual assessment of a feature space that has more than three dimensions is not possible, it becomes necessary to find an appropriate visualization scheme for such datasets. In this paper we present a new approach for dimension reduction to visualize high dimensional data. Our algorithm transforms high dimensional feature vectors into two-dimensional feature vectors under the constraints that the length of each vector is preserved and that the angles between vectors approximate the corresponding angles in the high dimensional space as good as possible, enabling us to come up with an efficient computing scheme.