Towards Understanding Human Similarity Perception in the Analysis of Large Sets of Scatter Plots

Pandey, Anshul Vikram; Krause, Josua; Felix, Cristian; Boy, Jeremy; Bertini, Enrico

doi:10.1145/2858036.2858155

Cited by 65 publications

(67 citation statements)

References 33 publications

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“…An underlying question of scatterplot similarity is how the perception of patterns in scatterplots by analysts can be modelled, and eventually described by descriptors. In [PKF+16], an experiment to assess how analysts describe specific patterns in scatterplots was presented, which found that these were not easy to model using Scagnostics features. The shape and model-based descriptors used in the proposed approach are one choice, but additional features could be defined (or even learned from training data) to describe patterns more compatible with user perception and interpretation.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…represent the perceptual similarity of patterns. Since they characterise the data according to appearance, they have some limitations [PKF+16]. Model-based descriptors (currently from regression models) are used to capture the relationship between points in a pattern (Step 2.2).…”

Section: Exploring Local Patterns In Scatterplotsmentioning

confidence: 99%

“…Pandey et al [PKF+16] showed that judging similarity between plots purely according to their appearance may be misleading. Therefore, thirdly, to avoid relevance feedback favouring shape-based descriptors, a colour-coding function to show similarity according to different descriptors was implemented.…”

Section: Similarity Visualisationmentioning

confidence: 99%

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Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces

Chegini

Shao

Gregor

et al. 2018

Computer Graphics Forum

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Analysts often use visualisation techniques like a scatterplot matrix (SPLOM) to explore multivariate datasets. The scatterplots of a SPLOM can help to identify and compare two-dimensional global patterns. However, local patterns which might only exist within subsets of records are typically much harder to identify and may go unnoticed among larger sets of plots in a SPLOM. This paper explores the notion of local patterns and presents a novel approach to visually select, search for, and compare local patterns in a multivariate dataset. Model-based and shape-based pattern descriptors are used to automatically compare local regions in scatterplots to assist in the discovery of similar local patterns. Mechanisms are provided to assess the level of similarity between local patterns and to rank similar patterns effectively. Moreover, a relevance feedback module is used to suggest potentially relevant local patterns to the user. The approach has been implemented in an interactive tool and demonstrated with two real-world datasets and use cases. It supports the discovery of potentially useful information such as clusters, functional dependencies between variables, and statistical relationships in subsets of data records and dimensions.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Exploring Local Patterns In Scatterplotsmentioning

confidence: 99%

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Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces

Chegini

Shao

Gregor

et al. 2018

Computer Graphics Forum

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“…For this analysis we chose the data collected by Pandey et al [33], which consists of human similarity judgments (13 participants) for a set of 247 single-color scatterplots. The similarity judgments were collected with a spatial arrangement interface in which scatterplot thumbnails are displayed in an "image carousel" and can be dragged and dropped into a large, initially empty, canvas.…”

Section: Computing Plot Similaritymentioning

confidence: 99%

Discriminability Tests for Visualization Effectiveness and Scalability

Veras

Collins

2020

IEEE Trans. Visual. Comput. Graphics

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The scalability of a particular visualization approach is limited by the ability for people to discern differences between plots made with different datasets. Ideally, when the data changes, the visualization changes in perceptible ways. This relation breaks down when there is a mismatch between the encoding and the character of the dataset being viewed. Unfortunately, visualizations are often designed and evaluated without fully exploring how they will respond to a wide variety of datasets. We explore the use of an image similarity measure, the Multi-Scale Structural Similarity Index (MS-SSIM), for testing the discriminability of a data visualization across a variety of datasets. MS-SSIM is able to capture the similarity of two visualizations across multiple scales, including low level granular changes and high level patterns. Significant data changes that are not captured by the MS-SSIM indicate visualizations of low discriminability and effectiveness. The measure's utility is demonstrated with two empirical studies. In the first, we compare human similarity judgments and MS-SSIM scores for a collection of scatterplots. In the second, we compute the discriminability values for a set of basic visualizations and compare them with empirical measurements of effectiveness. In both cases, the analyses show that the computational measure is able to approximate empirical results. Our approach can be used to rank competing encodings on their discriminability and to aid in selecting visualizations for a particular type of data distribution.

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“…Using scatterplots as an example, Wickham et al presented a discourse on the mutual complement of statistics and visualization [WCHB10]. More recently, Pandey et al examined the perception of scatterplot similarity [PKF*16], while Correll and Heer studied the perception of regression in scatterplots [CH17].…”

Section: Related Workmentioning

confidence: 99%

An Empirical Study on the Reliability of Perceiving Correlation Indices using Scatterplots

Sher

Bemis

Liccardi

et al. 2017

Computer Graphics Forum

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Scatterplots have been in use for about two centuries, primarily for observing the relationship between two variables and commonly for supporting correlation analysis. In this paper, we report an empirical study that examines how humans’ perception of correlation using scatterplots relates to the Pearson's product‐moment correlation coefficient (PPMCC) – a commonly used statistical measure of correlation. In particular, we study human participants’ estimation of correlation under different conditions, e.g., different PPMCC values, different densities of data points, different levels of symmetry of data enclosures, and different patterns of data distribution. As the participants were instructed to estimate the PPMCC of each stimulus scatterplot, the difference between the estimated and actual PPMCC is referred to as an offset. The results of the study show that varying PPMCC values, symmetry of data enclosure, or data distribution does have an impact on the average offsets, while only large variations in density cause an impact that is statistically significant. This study indicates that humans’ perception of correlation using scatterplots does not correlate with computed PPMCC in a consistent manner. The magnitude of offsets may be affected not only by the difference between individuals, but also by geometric features of data enclosures. It suggests that visualizing scatterplots does not provide adequate support to the task of retrieving their corresponding PPMCC indicators, while the underlying model of humans’ perception of correlation using scatterplots ought to feature other variables in addition to PPMCC. The paper also includes a theoretical discussion on the cost‐benefit of using scatterplots.

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Towards Understanding Human Similarity Perception in the Analysis of Large Sets of Scatter Plots

Cited by 65 publications

References 33 publications

Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces

Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces

Discriminability Tests for Visualization Effectiveness and Scalability

An Empirical Study on the Reliability of Perceiving Correlation Indices using Scatterplots

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