Visualizing Multivariate Functions, Data, and Distributions
IEEE Computer Graphics and Applications
Visualizing the behavior of higher dimensional dynamical systems
VIS '97 Proceedings of the 8th conference on Visualization '97
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Visualization Support for Data Mining
IEEE Expert: Intelligent Systems and Their Applications
Visualization of Analytically Defined Dynamical Systems
Dagstuhl '97, Scientific Visualization
Handbook of data mining and knowledge discovery
Improving the visualization of hierarchies with treemaps: design issues and experimentation
VIS '92 Proceedings of the 3rd conference on Visualization '92
A Visual Data Mining Environment
Visual Data Mining
ConnectedCharts: Explicit Visualization of Relationships between Data Graphics
Computer Graphics Forum
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"Scientific" Visualization has been driven by the need to visualize data sets that involve a large number of points and/or many "dimensions" or variables. However, many, or, perhaps all, fields of endeavor that use numeric data (including, finance, market research, management, manufacturing, process control, risk analysis, social services, health sciences, social sciences, physical sciences, computer science, applied mathematics, the engineering disciplines, and a host of other fields) deal with data sets of this type. Asking who can benefit from data visualization is like asking who can benefit from math or statistics. We present a hierarchical technique for visualizing truly multi-dimensional data that can be applied to any or all of these fields. The emphasis will be on visual statistical analysis of either discrete variables or continuous variables that have been sampled on, or binned to, a regular n-dimensional lattice.