Designing pixel-oriented visualization techniques: theory and applications

Keim, Daniel A.

doi:10.1109/2945.841121

Cited by 372 publications

(266 citation statements)

References 55 publications

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“…Over the last few decades, a variety of techniques for the visualization of such high-dimensional data have been proposed, many of which are reviewed by de Oliveira and Levkowitz (2003). Important techniques include iconographic displays such as Chernoff faces (Chernoff, 1973), pixel-based techniques (Keim, 2000), and techniques that represent the dimensions in the data as vertices in a graph (Battista et al, 1994). Most of these techniques simply provide tools to display more than two data dimensions, and leave the interpretation of the c 2008 Laurens van der Maaten and Geoffrey Hinton. data to the human observer.…”

Section: Introductionmentioning

confidence: 99%

How to Use t-SNE Effectively

Wattenberg

Viégas

Johnson

2016

Distill

705

489

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We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.

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Section: Introductionmentioning

confidence: 99%

How to Use t-SNE Effectively

Wattenberg

Viégas

Johnson

2016

Distill

705

489

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“…A less restricted version (with an arbitrary distance matrix) is considered in [18], where the goal is to order parallel coordinates.…”

Section: Problem 1 (Min Sum Ordering)mentioning

confidence: 99%

Visualizing Related Metabolic Pathways in Two and a Half Dimensions

Brandes

Dwyer

Schreiber

2004

Lecture Notes in Computer Science

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Abstract. We propose a method for visualizing a set of related metabolic pathways using 2 1 2 D graph drawing. Interdependent, twodimensional layouts of each pathway are stacked on top of each other so that biologists get a full picture of subtle and significant differences among the pathways. Layouts are determined by a global layout of the union of all pathway-representing graphs using a variant of the proven Sugiyama approach for layered graph drawing that allows edges to cross if they appear in different graphs.

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“…Visualization techniques have been proven to be of great value in supporting the data exploration process, since presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem solving and gaining deeper domain knowledge [11]. For the visual analysis of large data sets particularly pixel based techniques, like dense pixel displays or pixel based geo-spatial techniques, are a very powerful exploration tools.…”

Section: Introductionmentioning

confidence: 99%

Scalable Pixel Based Visual Data Exploration

Keim

Schneidewind

Sips

2007

Pixelization Paradigm

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Abstract. Pixel based visualization techniques have proven to be of high value in visual data exploration. Mapping data points to pixels not only allows the analysis and visualization of large data sets, but also provides an intuitive way to convert raw data into a graphical form. The graphical representation fosters new insights and encourages the formation and validation of new hypotheses to the end of better problem solving and gaining deeper domain knowledge. However, the ever increasing amount of information leads to new challenges for pixel-based techniques and concepts, especially if the number of data points significantly exceeds the available screen resolution. The paper focuses on ways to improve the scalability of pixel based techniques by proposing a multiresolution pixel-oriented visual exploration approach for large datasets. This approach combines clustering techniques with pixel-oriented mappings to preserve local clusters while providing space filling relevancedriven representations of the whole data set or portions of the data. The paper presents different application scenarios from the fields of financial analysis, geo-visualization, and network data analysis that clearly show the practical benefit of the multi-resolution approach for tackling the problem of scalability

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Designing pixel-oriented visualization techniques: theory and applications

Cited by 372 publications

References 55 publications

How to Use t-SNE Effectively

How to Use t-SNE Effectively

Visualizing Related Metabolic Pathways in Two and a Half Dimensions

Scalable Pixel Based Visual Data Exploration

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