: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering<i />

Jeon, Hyeon; Quadri, Ghulam Jilani; Lee, Hyunwook; Rosen, Paul; Szafir, Danielle Albers; Seo, Jinwook

doi:10.1109/tvcg.2023.3327201

Cited by 9 publications
(2 citation statements)

References 82 publications

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“…However, these heuristics are designed by data analysts grounded on mathematical principles rather than directly from perceptual judgments. A more recent work 40 uses an approach similar to ClustMe but considers cluster ambiguity measured with Shannon entropy of human judgments S1 instead of cluster separation. It also uses feature engineering to generate various aggregate heuristics of the GMM parameters and analyze factors at play in visual perception of cluster ambiguity.…”

Section: Related Workmentioning
confidence: 99%

ClustML: A measure of cluster pattern complexity in scatterplots learnt from human-labeled groupings
Hamza,
Ullah,
Baggag
et al. 2024
Information Visualization
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Visual quality measures (VQMs) are designed to support analysts by automatically detecting and quantifying patterns in visualizations. We propose a new VQM for visual grouping patterns in scatterplots, called ClustML, which is trained on previously collected human subject judgments. Our model encodes scatterplots in the parametric space of a Gaussian Mixture Model and uses a classifier trained on human judgment data to estimate the perceptual complexity of grouping patterns. The numbers of initial mixture components and final combined groups quantify visual cluster patterns in scatterplots. It improves on existing VQMs, first, by better estimating human judgments on two-Gaussian cluster patterns and, second, by giving higher accuracy when ranking general cluster patterns in scatterplots. We use it to analyze kinship data for genome-wide association studies, in which experts rely on the visual analysis of large sets of scatterplots. We make the benchmark datasets and the new VQM available for practical use and further improvements.

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Section: Related Workmentioning
confidence: 99%

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“…To explore these categories, we developed four scenarios for each type of support, totaling 12 distinct scenarios. We employed a stratified sampling method, drawing inspiration from previous studies [42,65]. Initially, we created 10 scenarios for each support category.…”

Section: Details Of Scenariosmentioning
confidence: 99%

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models
Ha,
Jeon,
Han
et al. 2024
Proceedings of the CHI Conference on Human Factors in Computing Systems
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Large language models (LLMs) have facilitated significant strides in generating conversational agents, enabling seamless, contextually relevant dialogues across diverse topics. However, the existing LLM-driven conversational agents have fixed personalities and functionalities, limiting their adaptability to individual user needs. Creating personalized agent personas with distinct expertise or traits can address this issue. Nonetheless, we lack knowledge of how people customize and interact with agent personas. In this

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Toward Constructing Frameworks for Task- and Design-Optimized Visualizations
Quadri
2024
IEEE Comput. Grap. Appl.
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: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering

Cited by 9 publications

References 82 publications

ClustML: A measure of cluster pattern complexity in scatterplots learnt from human-labeled groupings

ClustML: A measure of cluster pattern complexity in scatterplots learnt from human-labeled groupings

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

Toward Constructing Frameworks for Task- and Design-Optimized Visualizations

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