SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Geometry and topology for mesh generation
Geometry and topology for mesh generation
A human-computer cooperative system for effective high dimensional clustering
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Deflating the Dimensionality Curse Using Multiple Fractal Dimensions
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Spider Cursor: a simple versatile interaction tool for data visualization and exploration
GRAPHITE '05 Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
Visual text mining using association rules
Computers and Graphics
Visualizing gene co-expression as google maps
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Time-aware visualization of document collections
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Piecewise laplacian-based projection for interactive data exploration and organization
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
A framework for exploring multidimensional data with 3D projections
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
User-driven feature space transformation
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Projection (or dimensionality reduction) techniques have been used as a means to handling the growing dimensionality of data sets as well as providing a way to visualize information coded into point relationships. Their role is essential in data interpretation and simultaneous use of different projections and their visualizations improve data understanding and increase the level of confidence in the result. For that purpose, projections should be fast to allow multiple views of the same data set. In this work we present a novel fast technique for projecting multi-dimensional data sets into bidimensional (2D) spaces that preserves neighborhood relationships. Additionally, a new technique for improving 2D projections from multi-dimensional data is presented, that helps reduce the inherent loss of information yielded by dimensionality reduction. The results are stimulating and are presented in the form of comparative visualizations against known and new 2D projection techniques. Based on the projection improvement approach presented here, a new metric for quality of projection is also given, that matches well the visual perception of quality. We discuss the implication of using improved projections in visual exploration of large data sets and the role of interaction in visualization of projected subspaces.