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
POLARMAP - Efficient Visualisation of High Dimensional Data
IV '06 Proceedings of the conference on Information Visualization
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
MDSpolar: a new approach for dimension reduction to visualize high dimensional data
IDA'05 Proceedings of the 6th international conference on Advances in 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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Visualization of high-dimensional data is an important issue in data mining as it enhances the chance to selectively choose appropriate techniques for analyzing data. In this paper, two extensions to recent angle based multi-dimensional scaling techniques are presented. The first approach concerns the preprocessing of the data with the objective to lower the error of the subsequent mapping. The second aims at improving differentiability of angle based mappings by augmenting the target space by one additional dimension. Experimental results demonstrate the gain of efficiency in terms of layout quality and computational complexity.