RadViz++: Improvements on Radial-Based Visualizations

Pagliosa, Lucas de Carvalho; Telea, Alexandru

doi:10.3390/informatics6020016

Cited by 13 publications

(6 citation statements)

References 45 publications

(62 reference statements)

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“…Nonlinear techniques, such as UMAP, are generally more computationally expensive, but strive to represent local neighborhood information with minimal distortion. There are also a number of projection techniques that generally fit under the RadViz family [28], [29], [30]. RadViz is able to visualize multidimensional data in 2D by anchoring each feature around the perimeter of a circle, and leverages spring forces from those points to assign each instance a location inside the circle.…”

Section: Projectionsmentioning

confidence: 99%

UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data

Espadoto

Appleby

Suh

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection -the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this article we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.

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Section: Projectionsmentioning

confidence: 99%

UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data

Espadoto

Appleby

Suh

et al. 2023

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…According to Hoffman et al [25], this visualization method arranges the data instance nodes in a two-dimensional map from n-dimensional points. The study by Pagliosa et al [29] suggests that RadViz++ incorporate an icicle-plot metaphor into the existing RadViz visualization to show the clustering result of the data instance better. Zhou et al [30] improved the layout of the RadViz visualization by using the mean shift algorithm.…”

Section: Visualizationmentioning

confidence: 99%

A Visualization System for Exploring Logo Trend and Design Shape Patterns

Han

Lee

2020

Applied Sciences

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A logo is an effective way of expressing a brand’s identity and an essential element in conveying the values and image of the company. The development process of a competitive logo should be based on a design that is future-proof in a rapidly changing global market; hence, understanding the design trends for successful logo design is key. In this study, the design shape elements of logo trend models were analyzed and made into a database. Then, a trend analysis system was produced using radial visualization (RadViz) and circular parallel coordinates data visualization techniques. RadViz allows observation of clusters of logos that have similar shape elements, whereas with circular parallel coordinates plots, detailed information of the shape elements of each logo trend can be seen. Using the system, it was confirmed that shape elements—such as transformation to surface, overlapping, artificiality, concept of color and rhythm—play a major role in driving a trend. It was observed that trends change over time as various shape elements are added or removed. In addition, our study is expected to help predict the logo trend models that will come into style in the future. While similar efforts have been made in the past, our proposed system improves upon them by utilizing standard design elements as the categorizing criteria, using a unique combination of RadViz and circular parallel coordinates data visualization techniques. Using our system as a guideline, many users would be able to create logos that reflect what is trending.

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“…Moreover, the relationships among the data are not considered, so items with different quantities and the same proportion are projected to the same position. To address this drawback, RadViz++ [24] includes histograms over each attribute cell. The histograms show the data distribution and are linked with brushed data, thereby explaining ambiguity.…”

Section: Visualization Of Multivariate Datamentioning

confidence: 99%

AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data

et al. 2019

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Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis is a powerful technique for exploring unknown patterns since it retains the details of the original data and gives visual feedback to users. In this paper, we focus on air quality data and propose the AirInsight design, an interactive visual analytic system for recognizing, exploring, and summarizing regular patterns, as well as detecting, classifying, and interpreting abnormal cases. Based on the time-varying and multivariate features of air quality data, a dimension reduction method Composite Least Square Projection (CLSP) is proposed, which allows appreciating and interpreting the data patterns in the context of attributes. On the basis of the observed regular patterns, multiple abnormal cases are further detected, including the multivariate anomalies by the proposed Noise Hierarchical Clustering (NHC) method, abruptly changing timestamps by Time diversity (TD) indicator, and cities with unique patterns by the Geographical Surprise (GS) measure. Moreover, we combine TD and GS to group anomalies based on their underlying spatiotemporal correlations. AirInsight includes multiple coordinated views and rich interactive functions to provide contextual information from different aspects and facilitate a comprehensive understanding. In particular, a pair of glyphs are designed that provide a visual representation of the temporal variation in air quality conditions for a user-selected city. Experiments show that CLSP improves the accuracy of Least Square Projection (LSP) and that NHC has the ability to separate noises. Meanwhile, several case studies and task-based user evaluation demonstrate that our system is effective and practical for exploring and interpreting multivariate spatiotemporal patterns and anomalies in air quality data.

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RadViz++: Improvements on Radial-Based Visualizations

Cited by 13 publications

References 45 publications

UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data

UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data

A Visualization System for Exploring Logo Trend and Design Shape Patterns

AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data

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