Visualizing multi-dimensional clusters, trends, and outliers using star coordinates

Kandogan, Eser

doi:10.1145/502512.502530

Cited by 216 publications

(165 citation statements)

References 10 publications

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“…As cross-glyph comparisons can be difficult, they are not well-suited for discovering trends, distributions, and correlations. Axis-based techniques such as Parallel Coordinates [20] and Star Coordinates [22] map attributes to coordinate axes. However, the connecting edges can cause clutter and occlusion issues, which can lead to difficulties in interpretation [52].…”

Section: Multi-dimensional Data Visualizationmentioning

confidence: 99%

Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

Loorak

Périn

Collins³

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Heterogeneous multi-dimensional data are now sufficiently common that they can be referred to as ubiquitous. The most frequent approach to visualizing these data has been to propose new visualizations for representing these data. These new solutions are often inventive but tend to be unfamiliar. We take a different approach. We explore the possibility of extending well-known and familiar visualizations through including Heterogeneous Embedded Data Attributes (HEDA) in order to make familiar visualizations more powerful. We demonstrate how HEDA is a generic, interactive visualization component that can extend common visualization techniques while respecting the structure of the familiar layout. HEDA is a tabular visualization building block that enables individuals to visually observe, explore, and query their familiar visualizations through manipulation of embedded multivariate data. We describe the design space of HEDA by exploring its application to familiar visualizations in the D3 gallery. We characterize these familiar visualizations by the extent to which HEDA can facilitate data queries based on attribute reordering. Permanent repository link

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Section: Multi-dimensional Data Visualizationmentioning

confidence: 99%

Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

Loorak

Périn

Collins³

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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“…The last stage generates the final visualization of the reduced data. To do so, the star coordinates (SC) algorithm is used [27]. SC algorithm works as follows: first, each attribute is represented as a vector radiating from the center of a circle to its circumference.…”

Section: Fig 1 the Medvir Frameworkmentioning

confidence: 99%

MedVir: An Interactive Representation System of Multidimensional Medical Data Applied to Traumatic Brain Injury’s Rehabilitation Prediction

González

Gracia

Herrero

et al. 2014

Rough Sets and Intelligent Systems Paradigms

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Abstract. Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.

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“…We have implemented and built on two of these techniques, specifically parallel coordinates [3] and star coordinates [4].…”

Section: Multidimensional Visualizationmentioning

confidence: 99%

“…We show that R-trees are visualizable using hierarchical parallel coordinates, and introduce a method which builds upon Kandogan's [4], which we denote as hierarchical star coordinates. In both cases, we describe how to represent multidimensional data elements as well as bounding hyperboxes.…”

Section: Visualization Of Datasets Within R-treesmentioning

confidence: 99%

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Using R-Trees for Interactive Visualization of Large Multidimensional Datasets

Giménez

Rosenbaum

Hlawitschka

et al. 2010

Advances in Visual Computing

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Abstract. Large, multidimensional datasets are difficult to visualize and analyze. Visualization interfaces are constrained in resolution and dimension, so cluttering and problems of projecting many dimensions into the available low dimensions are inherent. Methods of real-time interaction facilitate analysis, but often these are not available due to the computational complexity required to use them. By organizing the dataset into a level-of-detail (LOD) hierarchy, our proposed method solves problems of both inefficient interaction and visual cluttering. We do this by introducing an implementation of R-trees for large multidimensional datasets. We introduce several useful methods for interaction, by queries and refinement, to explain the relevance of interaction and show that it can be done efficiently with R-trees. We examine the applicability of hierarchical parallel coordinates to datasets organized within an R-tree, and build upon previous work in hierarchical star coordinates to introduce a novel method for visualizing bounding hyperboxes of internal R-tree nodes. Finally, we examine two datasets using our proposed method and present and discuss results.

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Visualizing multi-dimensional clusters, trends, and outliers using star coordinates

Cited by 216 publications

References 10 publications

Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

MedVir: An Interactive Representation System of Multidimensional Medical Data Applied to Traumatic Brain Injury’s Rehabilitation Prediction

Using R-Trees for Interactive Visualization of Large Multidimensional Datasets

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