TY - GEN
T1 - Spectral-based contractible parallel coordinates
AU - Nohno, Koto
AU - Wu, Hsiang Yun
AU - Watanabe, Kazuho
AU - Takahashi, Shigeo
AU - Fujishiro, Issei
N1 - Publisher Copyright:
© 2014 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2014/9/18
Y1 - 2014/9/18
N2 - Parallel coordinates is well-known as a popular tool for visualizing the underlying relationships among variables in high-dimension datasets. However, this representation still suffers from visual clutter arising from intersections among poly line plots especially when the number of data samples and their associated dimension become high. This paper presents a method of alleviating such visual clutter by contracting multiple axes through the analysis of correlation between every pair of variables. In this method, we first construct a graph by connecting axis nodes with an edge weighted by data correlation between the corresponding pair of dimensions, and then reorder the multiple axes by projecting the nodes onto the primary axis obtained through the spectral graph analysis. This allows us to compose a dendrogram tree by recursively merging a pair of the closest axes one by one. Our visualization platform helps the visual interpretation of such axis contraction by plotting the principal component of each data sample along the composite axis. Smooth animation of the associated axis contraction and expansion has also been implemented to enhance the visual readability of behavior inherent in the given high-dimensional datasets.
AB - Parallel coordinates is well-known as a popular tool for visualizing the underlying relationships among variables in high-dimension datasets. However, this representation still suffers from visual clutter arising from intersections among poly line plots especially when the number of data samples and their associated dimension become high. This paper presents a method of alleviating such visual clutter by contracting multiple axes through the analysis of correlation between every pair of variables. In this method, we first construct a graph by connecting axis nodes with an edge weighted by data correlation between the corresponding pair of dimensions, and then reorder the multiple axes by projecting the nodes onto the primary axis obtained through the spectral graph analysis. This allows us to compose a dendrogram tree by recursively merging a pair of the closest axes one by one. Our visualization platform helps the visual interpretation of such axis contraction by plotting the principal component of each data sample along the composite axis. Smooth animation of the associated axis contraction and expansion has also been implemented to enhance the visual readability of behavior inherent in the given high-dimensional datasets.
KW - axis contraction
KW - dendrograms
KW - parallel coordinates
KW - spectral graph theory
UR - http://www.scopus.com/inward/record.url?scp=84912075377&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84912075377&partnerID=8YFLogxK
U2 - 10.1109/IV.2014.60
DO - 10.1109/IV.2014.60
M3 - Conference contribution
AN - SCOPUS:84912075377
T3 - Proceedings of the International Conference on Information Visualisation
SP - 7
EP - 12
BT - Proceedings - 2014 18th International Conference on Information Visualisation
A2 - Marchese, Francis T.
A2 - Cvek, Urska
A2 - Trutschl, Marjan
A2 - Grinstein, Georges
A2 - Ursyn, Anna
A2 - Venturini, Gilles
A2 - Wyeld, Theodor G.
A2 - Kenderdine, Sarah
A2 - Bouali, Fatma
A2 - Sarfraz, Muhammad
A2 - Banissi, Ebad
A2 - Bannatyne, Mark W. McK.
A2 - Geroimenko, Vladimir
A2 - Kenderdine, Sarah
A2 - Kenderdine, Sarah
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 18th International Conference on Information Visualisation: Visualisation, BioMedical Visualization, Visualisation on Built and Rural Environments and Geometric Modelling and Imaging, IV 2014
Y2 - 16 July 2014 through 18 July 2014
ER -