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
GTM: the generative topographic mapping
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Accelerating exact k-means algorithms with geometric reasoning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for the visualization of large and multivariate data sets
Self-Organizing neural networks
Self-Organizing Maps
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast multidimensional scaling through sampling, springs and interpolation
Information Visualization
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Steerable, Progressive Multidimensional Scaling
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Improving hybrid MDS with pivot-based searching
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Support Vector Machines for Visualization and Dimensionality Reduction
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Large Datasets Visualization with Neural Network Using Clustered Training Data
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
DEMScale: Large Scale MDS Accounting for a Ridge Operator and Demographic Variables
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
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A common task in data mining is the visualization of multivariate objects on scatterplots, allowing human observers to perceive subtle inter-relations in the dataset such as outliers, groupings or other regularities. Least- squares multidimensional scaling (MDS) is a well known Exploratory Data Analysis family of techniques that produce dissimilarity or distance preserving layouts in a nonlinear way. In this framework, the issue of visualizing large multidimensional datasets through MDS-based methods is addressed. An original scheme providing very accurate layouts of large datasets is introduced. It is a compromise between the computational complexity O(N5/2) and the accuracy of the solution that makes it suitable both for visualization of fairly large datasets and preprocessing in pattern recognition tasks.