The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
The visual display of quantitative information
The visual display of quantitative information
Envisioning information
Exploring N-dimensional databases
VIS '90 Proceedings of the 1st conference on Visualization '90
Shape coding of multidimensional data on a microcomputer display
VIS '90 Proceedings of the 1st conference on Visualization '90
Visualizing a scalar field on an N-dimensional lattice
VIS '90 Proceedings of the 1st conference on Visualization '90
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Visualizing high dimensional datasets and multivariate relations (tutorial AM-2)
Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
The Integrated Delivery of Large-Scale Data Mining: The ACSys Data Mining Project
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Advanced Engineering Informatics
CHIRP: a new classifier based on composite hypercubes on iterated random projections
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
HOV3: an approach to visual cluster analysis
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
The pattern classification based on fuzzy min-max neural network with new algorithm
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
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A hyperbox is a 2-dimensional depiction of an N-dimensional box (rectangular parallelepiped). This paper defines the visual syntax of hyperboxes, states some properties, and sketches two applications. Hyperboxes can be evocative visual names for tensors or multidimensional arrays in visual programming languages. They can also be used to simultaneously display all pairwise relationships in an N-dimensional dataset. We show that this can be helpful in choosing a sequence of dimension-reducing transformations that preserve interesting properties of the dataset.