Often multidimensional data are visualized by splitting n-D data to a set of low dimensional data. While it is useful it destroys integrity of n-D data, and leads to a shallow understanding complex n-D data. To mitigate this challenge a difficult perceptual task of assembling low-dimensional visualized pieces to the whole n-D vectors must be solved. Another way is a lossy dimension reduction by mapping n-D vectors to 2-D vectors (e.g., Principal Component Analysis). Such 2-D vectors carry only a part of information from n-D vectors, without a way to restore n-D vectors exactly from it. An alternative way for deeper understanding of n-D data is visual representations in 2-D that fully preserve n-D data. Methods of Parallel and Radial coordin...
Multidimensional relationships exist in almost any discipline. There are numerous visualization meth...
These results will show that the use of Linear General Line Coordinates (GLC-L) can visualize multid...
Many graphical methods for displaying multivariate data consist of arrangements of multiple displays...
Preserving all multidimensional data in two-dimensional visualization is a long-standing problem in ...
Visualization and interaction of multidimensional data always requires optimized solutions to integr...
Abstract: The exploration of multidimensional datasets of all possible sizes and dimensions is a lon...
The major challenges in visualization of large n-D data in 2-D are in supporting the most efficient ...
The collaborative approach is a natural way to enhance visualization and visual analytics methods. T...
In this paper I analysis about the visualization techniques of large set of data with parallel coord...
The exploration and visualization of hierarchical and multi-dimensional datasets is a fundamental ch...
Higher-dimensional data, which is becoming common in many disciplines due to big data problems, are ...
While knowledge discovery and n-D data visualization procedures are often efficient, the loss of inf...
This book combines the advantages of high-dimensional data visualization and machine learning in the...
The three dimensional parallel coordinate plot (3-D PCP) is a visualization method to detect hidd...
The three dimensional parallel coordinate plot (3-D PCP) is a visualization method to detect hidd...
Multidimensional relationships exist in almost any discipline. There are numerous visualization meth...
These results will show that the use of Linear General Line Coordinates (GLC-L) can visualize multid...
Many graphical methods for displaying multivariate data consist of arrangements of multiple displays...
Preserving all multidimensional data in two-dimensional visualization is a long-standing problem in ...
Visualization and interaction of multidimensional data always requires optimized solutions to integr...
Abstract: The exploration of multidimensional datasets of all possible sizes and dimensions is a lon...
The major challenges in visualization of large n-D data in 2-D are in supporting the most efficient ...
The collaborative approach is a natural way to enhance visualization and visual analytics methods. T...
In this paper I analysis about the visualization techniques of large set of data with parallel coord...
The exploration and visualization of hierarchical and multi-dimensional datasets is a fundamental ch...
Higher-dimensional data, which is becoming common in many disciplines due to big data problems, are ...
While knowledge discovery and n-D data visualization procedures are often efficient, the loss of inf...
This book combines the advantages of high-dimensional data visualization and machine learning in the...
The three dimensional parallel coordinate plot (3-D PCP) is a visualization method to detect hidd...
The three dimensional parallel coordinate plot (3-D PCP) is a visualization method to detect hidd...
Multidimensional relationships exist in almost any discipline. There are numerous visualization meth...
These results will show that the use of Linear General Line Coordinates (GLC-L) can visualize multid...
Many graphical methods for displaying multivariate data consist of arrangements of multiple displays...