A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering
This repository contains the source code for the CLAMS algorithm, which is a visual quality measure for estimating perceptual variability in visual clustering. The algorithm is contributed with a paper titled "CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering", which will be published in IEEE Transactions on Visualization and Computer Graphics (TVCG, Proc. VIS 2023).
The original implementation of CLAMS is messy and has several dependency issue that are not easy to resolve. Therefore, we re-implemented the algorithm and made it available for the community. The re-implementation is based on the original implementation, but it is more readable and easy to install/use. However, it may not produce the exact same results as the original implementation.
Please contact Hyeon Jeon (hj@hcil.snu.ac.kr) to access the original implementation.
Hyeon Jeon, Ghulam Jilani Quadri, Hyunwook Lee, Paul Rosen, Danielle Albers Szafir, and Jinwook Seo, "CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering," IEEE Transactions on Visualization and Computer Graphics (TVCG, Proc. VIS 2023), to appear.
- Hyeon Jeon and Ghulam Jilani Quadri contributed equally to this work.
- Received the Best Paper Honorable Mention Award at IEEE VIS 2023.
@article{jeon23tvcg2,
author = {Jeon, Hyeon and Quadri, Ghulam Jilani and Lee, Hyunwook and Rosen, Paul and Szafir, Danielle Albers and Seo, Jinwook},
journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG, Proc. VIS 2023)},
title = {CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering},
year = {2023},
volume = {},
number = {},
pages = {},
note = {to appear.}
}