Grid visualizations are widely used in many applications to visually explain a set of data and their proximity relationships. However, existing layout methods face difficulties when dealing with the inherent cluster structures within the data. To address this issue, we propose a cluster-aware grid layout method that aims to better preserve cluster structures by simultaneously considering proximity, compactness, and convexity in the optimization process. Our method utilizes a hybrid optimization strategy that consists of two phases. The global phase aims to balance proximity and compactness within each cluster, while the local phase ensures the convexity of cluster shapes. We evaluate the proposed grid layout method through a series of quantitative experiments and two use cases, demonstrating its effectiveness in preserving cluster structures and facilitating analysis task.
Yuxing Zhou, Weikai Yang, Jiashu Chen, Changjian Chen, Zhiyang Shen, Xiaonan Luo, Lingyun Yu, Shixia Liu. “Cluster-Aware Grid Layout,” in IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 1, pp. 240-250, Jan. 2024, doi: 10.1109/TVCG.2023.3326934.