Kwon Ko

All Publications

• Distortion-Aware Brushing for Reliable Cluster Analysis in Multidimensional Projections IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS Jeon, H., Aupetit, M., Lee, S., Ko, K., Kim, Y., Quadri, G., Seo, J. 2026; 32 (2): 2165-2182

  Abstract

  Brushing is a common interaction technique in 2D scatterplots, allowing users to select clustered points within a continuous, enclosed region for further analysis or filtering. However, applying conventional brushing to 2D representations of multidimensional (MD) data, i.e., Multidimensional Projections (MDPs), can lead to unreliable cluster analysis due to MDP-induced distortions that inaccurately represent the cluster structure of the original MD data. To alleviate this problem, we introduce a novel brushing technique for MDPs called Distortion-aware brushing. As users perform brushing, Distortion-aware brushing correct distortions around the currently brushed points by dynamically relocating points in the projection, pulling data points close to the brushed points in MD space while pushing distant ones apart. This dynamic adjustment helps users brush MD clusters more accurately, leading to more reliable cluster analysis. Our user studies with 24 participants show that Distortion-aware brushing significantly outperforms previous brushing techniques for MDPs in accurately separating clusters in the MD space and remains robust against distortions. We further demonstrate the effectiveness of our technique through two use cases: (1) conducting cluster analysis of geospatial data and (2) interactively labeling MD clusters.

  View details for DOI 10.1109/TVCG.2025.3615314

  View details for Web of Science ID 001682688000016

  View details for PubMedID 41021966

• UMATO: Bridging Local and Global Structures for Reliable Visual Analytics With Dimensionality Reduction IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS Jeon, H., Ko, K., Lee, S., Hyun, J., Yang, T., Go, G., Jo, J., Seo, J. 2025; 31 (12): 10503-10520

  Abstract

  Due to the intrinsic complexity of high-dimensional (HD) data, dimensionality reduction (DR) techniques cannot preserve all the structural characteristics of the original data. Therefore, DR techniques focus on preserving either local neighborhood structures (local techniques) or global structures such as pairwise distances between points (global techniques). However, both approaches can mislead analysts to erroneous conclusions about the overall arrangement of manifolds in HD data. For example, local techniques may exaggerate the compactness of individual manifolds, while global techniques may fail to separate clusters that are well-separated in the original space. In this research, we provide a deeper insight into Uniform Manifold Approximation with Two-phase Optimization (UMATO), a DR technique that addresses this problem by effectively capturing local and global structures. UMATO achieves this by dividing the optimization process of UMAP into two phases. In the first phase, it constructs a skeletal layout using representative points, and in the second phase, it projects the remaining points while preserving the regional characteristics. Quantitative experiments validate that UMATO outperforms widely used DR techniques, including UMAP, in terms of global structure preservation, with a slight loss in local structure. We also confirm that UMATO outperforms baseline techniques in terms of scalability and stability against initialization and subsampling, making it more effective for reliable HD data analysis. Finally, we present a case study and a qualitative demonstration that highlight UMATO's effectiveness in generating faithful projections, enhancing the overall reliability of visual analytics using DR.

  View details for DOI 10.1109/TVCG.2025.3602735

  View details for Web of Science ID 001611559200003

  View details for PubMedID 40853803

• Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models Ko, H., Jeon, H., Park, G., Kim, D., Kim, N., Kim, J., Seo, J., ACM ASSOC COMPUTING MACHINERY. 2024

  View details for DOI 10.1145/3613904.3642943

  View details for Web of Science ID 001266059704026

• ChatGPT in Data Visualization Education: A Student Perspective Kim, N., Ko, H., Myers, G., Bach, B., IEEE COMPUTER SOC IEEE COMPUTER SOC. 2024: 109-120

  View details for DOI 10.1109/VL/HCC60511.2024.00022

  View details for Web of Science ID 001368090400012

• Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works Ko, H., Park, G., Jeon, H., Jo, J., Kim, J., Seo, J., ACM ASSOC COMPUTING MACHINERY. 2023: 919-933

  View details for DOI 10.1145/3581641.3584078

  View details for Web of Science ID 001302573800064

• <i>ZADU</i>: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings Jeon, H., Cho, A., Jang, J., Lee, S., Hyun, J., Ko, H., Jo, J., Seo, J., IEEE IEEE COMPUTER SOC. 2023: 196-200

  View details for DOI 10.1109/VIS54172.2023.00048

  View details for Web of Science ID 001137142800040

• Measuring and Explaining the Inter-Cluster Reliability of Multidimensional Projections. IEEE transactions on visualization and computer graphics Jeon, H., Ko, H. K., Jo, J., Kim, Y., Seo, J. 2022; 28 (1): 551-561

  Abstract

  We propose Steadiness and Cohesiveness, two novel metrics to measure the inter-cluster reliability of multidimensional projection (MDP), specifically how well the inter-cluster structures are preserved between the original high-dimensional space and the low-dimensional projection space. Measuring inter-cluster reliability is crucial as it directly affects how well inter-cluster tasks (e.g., identifying cluster relationships in the original space from a projected view) can be conducted; however, despite the importance of inter-cluster tasks, we found that previous metrics, such as Trustworthiness and Continuity, fail to measure inter-cluster reliability. Our metrics consider two aspects of the inter-cluster reliability: Steadiness measures the extent to which clusters in the projected space form clusters in the original space, and Cohesiveness measures the opposite. They extract random clusters with arbitrary shapes and positions in one space and evaluate how much the clusters are stretched or dispersed in the other space. Furthermore, our metrics can quantify pointwise distortions, allowing for the visualization of inter-cluster reliability in a projection, which we call a reliability map. Through quantitative experiments, we verify that our metrics precisely capture the distortions that harm inter-cluster reliability while previous metrics have difficulty capturing the distortions. A case study also demonstrates that our metrics and the reliability map 1) support users in selecting the proper projection techniques or hyperparameters and 2) prevent misinterpretation while performing inter-cluster tasks, thus allow an adequate identification of inter-cluster structure.

  View details for DOI 10.1109/TVCG.2021.3114833

  View details for PubMedID 34587063

• We-toon: A Communication Support System between Writers and Artists in Collaborative Webtoon Sketch Revision Ko, H., An, S., Park, G., Kim, S., Kim, D., Kim, B., Jo, J., Seo, J., ACM ASSOC COMPUTING MACHINERY. 2022

  View details for DOI 10.1145/3526113.3545612

  View details for Web of Science ID 001046841800001

• Uniform Manifold Approximation with Two-phase Optimization Jeon, H., Ko, H., Lee, S., Jo, J., Seo, J., IEEE Comp Soc IEEE COMPUTER SOC. 2022: 80-84

  View details for DOI 10.1109/VIS54862.2022.00025

  View details for Web of Science ID 000904323300017

• Mixed-Initiative Approach to Extract Data from Pictures of Medical Invoice Jung, S., Choe, K., Park, S., Ko, H., Kim, Y., Seo, J., IEEE COMP SOC IEEE COMPUTER SOC. 2021: 111-115

  View details for DOI 10.1109/PacificVis52677.2021.00022

  View details for Web of Science ID 000680829900015

• Progressive Uniform Manifold Approximation and Projection EuroVis (Short Papers) Ko, H., Jo, J., Seo, J. 2020

  View details for DOI 10.2312/evs.20201061