Geono-Cluster: Interactive Visual Cluster Analysis for Biologists

Das, Subhajit; Saket, Bahador; Kwon, Bum Chul; Endert, Alex

doi:10.1109/tvcg.2020.3002166

Cited by 11 publications

(8 citation statements)

References 35 publications

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“…These typically present approaches where human involvement and trust are critical for utilization. As a result, several papers report bespoke, complex evaluation methodologies that go beyond more traditional pair analytics [KBJ*20] and observational studies [CYL*20;DSKE20;LPH*20] that are also used. Dasgupta et al [DLW*17] report on a controlled lab experiment that aims to evaluate how much users trust a bespoke analysis tool versus a traditional data analysis tool of their choice (like R or Excel).…”

Section: Bio-medical Applicationsmentioning

confidence: 99%

“…For example, there were cases where filtering was done using indirect manipulation (e.g., [MQB19;PNKC20]). Direct and indirect manipulation techniques provide different affordances and outcomes [Shn97], and a number of systems used a combination of both direct and indirect manipulation techniques (e.g., [CRH*19;DSKE20]). The impact of using different manipulation techniques has not been studied in the context of HCML and represents an avenue of potential future work.…”

Section: Directnessmentioning

confidence: 99%

“…14]), they do not appropriately capture several typical HCML tasks. The tasks listed here build on work byLiu et al [LSL*17] and were iteratively compiled during paper coding.Following the data and visual analytics pipelines, understood (e.g., [CWZ*19; MQB19; XCK*20]); and used (e.g.,[DSKE20; HHC*19;HKBE12]). Another task after training is the refinement of results (e.g.,…”

mentioning

confidence: 99%

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A Survey of Human‐Centered Evaluations in Human‐Centered Machine Learning

Sperrle

El‐Assady

Guo

et al. 2021

Computer Graphics Forum

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Visual analytics systems integrate interactive visualizations and machine learning to enable expert users to solve complex analysis tasks. Applications combine techniques from various fields of research and are consequently not trivial to evaluate. The result is a lack of structure and comparability between evaluations. In this survey, we provide a comprehensive overview of evaluations in the field of human‐centered machine learning. We particularly focus on human‐related factors that influence trust, interpretability, and explainability. We analyze the evaluations presented in papers from top conferences and journals in information visualization and human‐computer interaction to provide a systematic review of their setup and findings. From this survey, we distill design dimensions for structured evaluations, identify evaluation gaps, and derive future research opportunities.

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Section: Bio-medical Applicationsmentioning

confidence: 99%

Section: Directnessmentioning

confidence: 99%

mentioning

confidence: 99%

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A Survey of Human‐Centered Evaluations in Human‐Centered Machine Learning

Sperrle

El‐Assady

Guo

et al. 2021

Computer Graphics Forum

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show abstract

“…There is a wide variety of knowledge to implement the above models. Specifically, the knowledge can be standard procedures [21] and linguistic rules [48] collected from domain literature, relationships between samples [16,51,74] specified by users, constraints distilled from expert experiences [46], numeric features calculated based on pre-collected samples [72,73], etc. Besides, many recent works attempt to utilize knowledge involved in off-the-shelf digital resources, such as ontology [41,59], corpus [82], knowledge graphs [8,37], pre-trained models (e.g., knowledge distillation) [75], etc.…”

Section: Knowledge-assisted Visual Analyticsmentioning

confidence: 99%

Incorporation of Human Knowledge Into Data Embeddings to Improve Pattern Significance and Interpretability

Zhou

2022

IEEE Trans. Visual. Comput. Graphics

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2 a1 b1 c1 b2 a2 c1 c2 Fig. 1. The visualization system interface that consists of three components: (a) Knowledge Editor (KE), (b) Data Projector (DP), and (c) Pattern Explainer (PE). The analyst is analyzing the Covid-19 dataset [2] that consists of 173 sequences, each recording the number of monthly-confirmed cases (per million people) from May 2020 to April 2021 of a country. The analyst creates six country groups (see colored tree nodes) according to their knowledge using (a) and incorporates the class information into the embeddings of countries to generate the projection with clear and class-consistent visual structures that foreshadow underlying data patterns. (b2)The embedding projection without knowledge where does not exist visual structure.

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“…There is rich literature on visual exploration of clustering [5,19,21,29,34] and methods that leverage unsupervised clustering [2,3,17,25]. More recent methods demonstrate the advantage of providing a feedback loop to the domain expert, making clustering human-centered.…”

Section: Interactive Clusteringmentioning

confidence: 99%

PRAGMA: Interactively Constructing Functional Brain Parcellations

Bayrak¹,

Nhung²,

Hansen³

et al. 2020

Preprint

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Figure 1: PRAGMA is an interactive tool for constructing scan-specific brain parcellations from mainstream atlases. Its interface incorporates five complementary visuals: (A) abstract view of the hierarchical clustering of the brain through a node-link diagram, (B1) similarity of fMRI signal time-courses represented with a line plot with a shaded confidence interval, (B2) homogeneity glyphs within nodes, (C1) functional connectivity depicted as a chord diagram and (C2) anatomical location of parcels viewed on an orthographic template.

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Geono-Cluster: Interactive Visual Cluster Analysis for Biologists

Cited by 11 publications

References 35 publications

A Survey of Human‐Centered Evaluations in Human‐Centered Machine Learning

A Survey of Human‐Centered Evaluations in Human‐Centered Machine Learning

Incorporation of Human Knowledge Into Data Embeddings to Improve Pattern Significance and Interpretability

PRAGMA: Interactively Constructing Functional Brain Parcellations

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