# InfoClus: Informative Clustering of High-dimensional Data Embeddings

arXiv: https://arxiv.org/abs/2504.11089 · HTML: https://mlsanity.com/paper/2504.11089/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections](https://mlsanity.com/paper/2002.06910/) (2020)
- [DimReader: Axis lines that explain non-linear projections](https://mlsanity.com/paper/1710.00992/) (2017)
- [A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration](https://mlsanity.com/paper/1811.12199/) (2018)
- [UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data](https://mlsanity.com/paper/2111.01744/) (2021)
- [Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data](https://mlsanity.com/paper/2108.08706/) (2021)
- [DimBridge: Interactive Explanation of Visual Patterns in Dimensionality Reductions with Predicate Logic](https://mlsanity.com/paper/2404.07386/) (2024)
- [DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine](https://mlsanity.com/paper/2402.06885/) (2024)
- [VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region Annotation](https://mlsanity.com/paper/2406.04808/) (2024)

## Abstract (arXiv)

Developing an understanding of high-dimensional data can be facilitated by visualizing that data using dimensionality reduction. However, the low-dimensional embeddings are often difficult to interpret. To facilitate the exploration and interpretation of low-dimensional embeddings, we introduce a new concept named partitioning with explanations. The idea is to partition the data shown through the embedding into groups, each of which is given a sparse explanation using the original high-dimensional attributes. We introduce an objective function that quantifies how much we can learn through observing the explanations of the data partitioning, using information theory, and also how complex the explanations are. Through parameterization of the complexity, we can tune the solutions towards the desired granularity. We propose InfoClus, which optimizes the partitioning and explanations jointly, through greedy search constrained over a hierarchical clustering. We conduct a qualitative and quantitative analysis of InfoClus on three data sets. We contrast the results on the Cytometry data with published manual analysis results, and compare with two other recent methods for explaining embeddings (RVX and VERA). These comparisons highlight that InfoClus has distinct advantages over existing procedures and methods. We find that InfoClus can automatically create good starting points for the analysis of dimensionality-reduction-based scatter plots.