Data Visualization through Graph Drawing
George Michailidis,Jan de Leeuw +1 more
TL;DR: A general graph drawing framework is introduced, the corresponding mathematical problem defined and an algorithmic approach for solving the necessary optimization problem discussed.
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Abstract: In this paper the problem of visualizing categorical multivariate data sets is considered. By representing the data as the adjacency matrix of an appropriately defined bipartite graph, the problem is transformed to one of graph drawing. A general graph drawing framework is introduced, the corresponding mathematical problem defined and an algorithmic approach for solving the necessary optimization problem discussed. The new approach is illustrated through several examples.
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Citations
Principal Components and Extensions
George Michailidis
- 15 Oct 2005
TL;DR: In this article, the authors discuss some extensions of principal components analysis that deal with categorical multivariate data, and briefly discuss various approaches that try to enable the techniques that handle nonlinearities in the data.
1
Effects of different periodontal interventions on the risk of adverse pregnancy outcomes in pregnant women: a systematic review and network meta-analysis of randomized controlled trials
Jianru Wu,Jingying Wu,Bi‐Yu Tang,Ze Zhang,Fenfang Wei,Dingbiao Yu,Limin Li,Zhao Yue,Sheng Wang,Wenyu Wu,Xiang Hong +10 more
TL;DR: This systematic review and network meta-analysis of 20 randomized controlled trials found that certain periodontal treatment interventions, including SRP + CR and SRP + CR + TP, significantly reduced the risk of preterm birth and preterm birth and/or low birth weight in pregnant women.
1
Principal Components and Extensions
George Michailidis
- 29 Sep 2014
TL;DR: This paper discusses some extensions of principal components analysis that deal with categorical multivariate data and briefly discusses various approaches that try to enable the techniques that handle nonlinearities in the data.
Data analysis with intersection graphs
Valter Vairinhos,Victor Lobo,P. Galindo Villardón +2 more
- 01 Jan 2013
TL;DR: It is shown that this representation of data as an intersection graph allows an easy and intuitive geometric interpretation of data observations, groups of observations, and results of multivariate data analysis techniques such as biplots, principal components, cluster analysis, or multidimensional scaling.
References
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Graph Drawing: Algorithms for the Visualization of Graphs
Giuseppe Di Battista,Peter Eades,Roberto Tamassia,Ioannis G. Tollis +3 more
- 23 Jul 1998
TL;DR: In this paper, the authors describe fundamental algorithmic techniques for constructing drawings of graphs and provide an accurate, accessible reflection of the rapidly expanding field of graph drawing, using a reference manual.
1.9K
Graph visualization and navigation in information visualization: A survey
TL;DR: This is a survey on graph visualization and navigation techniques, as used in information visualization, which approaches the results of traditional graph drawing from a different perspective.
Methods for Visual Understanding of Hierarchical System Structures
Kozo Sugiyama,Shojiro Tagawa,Mitsuhiko Toda +2 more
- 01 Feb 1981
TL;DR: Two kinds of new methods are developed to obtain effective representations of hierarchies automatically: theoretical and heuristic methods that determine the positions of vertices in two steps to improve the readability of drawings.
1.4K
A Heuristic for Graph Drawing
Peter Eades
- 01 Jan 1984
TL;DR: Researchers propose a heuristic method for graph drawing, presenting a successful approach to visually representing complex networks, with implications for various fields, including computer science, mathematics, and information visualization.
1.3K
How to Draw a Graph
TL;DR: In this paper, the authors define nodally 3-connected graphs as simple and non-separable graphs, and show how to obtain a convex representation of such graphs without Kuratowski subgraphs.