# ClustML: A Measure of Cluster Pattern Complexity in Scatterplots Learnt from Human-labeled Groupings

arXiv: https://arxiv.org/abs/2106.00599 · HTML: https://mlsanity.com/paper/2106.00599/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 66 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [Clustrophile 2: Guided Visual Clustering Analysis](https://mlsanity.com/paper/1804.03048/) (2018)
- [Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology](https://mlsanity.com/paper/2007.13872/) (2020)
- [The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspaces](https://mlsanity.com/paper/1603.04781/) (2016)

## Built on by (most cited first, 2 of 2)

- [Measuring the Validity of Clustering Validation Datasets](https://mlsanity.com/paper/2503.01097/) (2025)
- [HPSCAN: Human Perception-Based Scattered Data Clustering](https://mlsanity.com/paper/2304.14185/) (2023)

## Abstract (arXiv)

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. 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.