Paper Info
Title
Scatterplot layout for high-dimensional data visualization
Abstract
Multi-dimensional data visualization is an important research topic that has been receiving increasing attention. Several techniques that apply scatterplot matrices have been proposed to represent multi-dimensional data as a collection of two-dimensional data visualization spaces. Typically, when using the scatterplot-based approach it is easier to understand relations between particular pairs of dimensions, but it often requires too large display spaces to display all possible scatterplots. This paper presents a technique to display meaningful sets of scatterplots generated from high-dimensional datasets. Our technique first evaluates all possible scatterplots generated from high-dimensional datasets, and selects meaningful sets. It then calculates the similarity between arbitrary pairs of the selected scatterplots, and places relevant scatterplots closer together in the display space while they never overlap each other. This design policy makes users easier to visually compare relevant sets of scatterplots. This paper presents algorithms to place the scatterplots by the combination of ideal position calculation and rectangle packing algorithms, and two examples demonstrating the effectiveness of the presented technique.
Year
DOI
Venue
2015
10.1007/s12650-014-0230-5
J. Visualization
Keywords
Field
DocType
Visualization,Scatterplot,Isomap,Force-directed graph layout,Rectangle packing
Data visualization,High dimensional data visualization,Computer graphics (images),Computer science,Matrix (mathematics),Visualization,Artificial intelligence,Classical mechanics,Machine learning,Rectangle packing,Isomap
Journal
Volume
Issue
ISSN
18
1
1343-8875
Citations 
PageRank 
References 
5
0.46
16
Authors
6
Name
Order
Citations
PageRank
Yunzhu Zheng150.80
Haruka Suematsu290.90
Takayuki Itoh350365.85
Ryohei Fujimaki419316.93
Satoshi Morinaga528846.89
Kawahara, Yoshinobu631731.30