Cluster-Based Visual Abstraction for Multivariate Scatterplots

Liao, Hongsen; Wu, Yingcai; Chen, Li; Chen, Wei

doi:10.1109/tvcg.2017.2754480

Cited by 28 publications

(16 citation statements)

References 37 publications

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“…ProxiLens by Heulot et al [16] allows within-cluster and between-cluster analysis by automatically moving false low-dimensional neighbors to the border of an interactive lens. Liao et al [29] use abstract glyphs in combination with a tabular view to enable a cluster-focused analysis of multivariate scatterplots. The t-viSNE technique by Chatzimparmpas et al [5] allows users to explore and interpret t-SNE scatterplots in a dashboard that shows information related to the preservation of neighborhoods or pairwise distances.…”

Section: Exploration Of Embedding Spacesmentioning

confidence: 99%

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Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Eckelt¹,

Hinterreiter²,

Adelberger³

et al. 2021

Preprint

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In this work, we propose an interactive visual approach for the exploration of structural relationships in embeddings of high-dimensional data. These structural relationships, such as item sequences, associations of items with groups, and hierarchies between groups of items, are defining properties of many real-world datasets. Nevertheless, most existing methods for the visual exploration of embeddings treat these structures as second-class citizens or do not take them into account at all. In our proposed analysis workflow, users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted. The original high-dimensional data for single items, groups of items, or differences between connected items and groups is accessible through additional summary visualizations. We carefully tailored these summary and difference visualizations to the various data types and semantic contexts. During their exploratory analysis, users can externalize their insights by setting up additional groups and relationships between items and/or groups, thereby creating graphs that represent visual data stories. We demonstrate the utility and potential impact of our approach by means of two use cases and multiple examples from various domains.

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Section: Exploration Of Embedding Spacesmentioning

confidence: 99%

“…Joia et al [22] place textual representations of the most important attributes inside the concave hulls of clusters. Liao et al [29] use radial glyphs as abstract visual summaries for clusters in multivariate scatterplots. Júnior et al [24] propose the use of star plots as visual summaries for the analysis of feature spaces.…”

Section: Summary Visualizationmentioning

confidence: 99%

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Eckelt¹,

Hinterreiter²,

Adelberger³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Other important DR techniques include LLE [RS00], LE [BN02], LTSA [ZZ04], etc. Many works exist for explaining the DR results [FKM19, LWCC17, FGS18, CMK20]. All these techniques, however, are to maintain global relationships [SPT19] and cannot be used to explore subset patterns that are often associated with few features.…”

Section: Related Workmentioning

confidence: 99%

Exploring Multi‐dimensional Data via Subset Embedding

Xie

Tao

et al. 2021

Computer Graphics Forum

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Multi‐dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly‐formatted embeddings. We implement the SEN as multiple subnets with separate loss functions. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features, thus achieving accurate pattern exploration, which in most cases is searching for subsets having similar values on few features. Moreover, each subnet is a fully‐connected neural network with one hidden layer. The simple structure brings high training efficiency. We integrate the SEN into a visualization system that achieves a 3‐step workflow. Specifically, analysts (1) partition the given dataset into subsets, (2) select portions in a projected latent space created using the SEN, and (3) determine the existence of patterns within selected subsets. Generally, the system combines visualizations, interactions, automatic methods, and quantitative measures to balance the exploration flexibility and operation efficiency, and improve the interpretability and faithfulness of the identified patterns. Case studies and quantitative experiments on multiple open datasets demonstrate the general applicability and effectiveness of our approach.

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“…In summary, we present the first attempt to understand the perceptual bias of scatterplots caused by geometric scaling. We contribute a carefully designed evaluation and a series of instructive findings, which may bring new considerations to the design decisions of scatterplots in various creation, exploration, and sharing scenarios [41,74,84,85].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Perceptual Bias During Geometric Scaling of Scatterplots

Wei

Mei

Zhao

et al. 2020

IEEE Trans. Visual. Comput. Graphics

Self Cite

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Scatterplots are frequently scaled to fit display areas in multi-view and multi-device data analysis environments. A common method used for scaling is to enlarge or shrink the entire scatterplot together with the inside points synchronously and proportionally. This process is called geometric scaling. However, geometric scaling of scatterplots may cause a perceptual bias, that is, the perceived and physical values of visual features may be dissociated with respect to geometric scaling. For example, if a scatterplot is projected from a laptop to a large projector screen, then observers may feel that the scatterplot shown on the projector has fewer points than that viewed on the laptop. This paper presents an evaluation study on the perceptual bias of visual features in scatterplots caused by geometric scaling. The study focuses on three fundamental visual features (i.e., numerosity, correlation, and cluster separation) and three hypotheses that are formulated on the basis of our experience. We carefully design three controlled experiments by using well-prepared synthetic data and recruit participants to complete the experiments on the basis of their subjective experience. With a detailed analysis of the experimental results, we obtain a set of instructive findings. First, geometric scaling causes a bias that has a linear relationship with the scale ratio. Second, no significant difference exists between the biases measured from normally and uniformly distributed scatterplots. Third, changing the point radius can correct the bias to a certain extent. These findings can be used to inspire the design decisions of scatterplots in various scenarios.

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Cluster-Based Visual Abstraction for Multivariate Scatterplots

Cited by 28 publications

References 37 publications

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Exploring Multi‐dimensional Data via Subset Embedding

Evaluating Perceptual Bias During Geometric Scaling of Scatterplots

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