# Visualizing Overlapping Biclusterings and Boolean Matrix Factorizations

arXiv: https://arxiv.org/abs/2307.07396 · HTML: https://mlsanity.com/paper/2307.07396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 14 Jul 2023.

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

- [Recent Developments in Boolean Matrix Factorization](https://mlsanity.com/paper/2012.03127/) (2020)
- [The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization](https://mlsanity.com/paper/1906.09722/) (2019)
- [Boolean matrix factorization meets consecutive ones property](https://mlsanity.com/paper/1901.05797/) (2019)

## Abstract (arXiv)

Finding (bi-)clusters in bipartite graphs is a popular data analysis approach. Analysts typically want to visualize the clusters, which is simple as long as the clusters are disjoint. However, many modern algorithms find overlapping clusters, making visualization more complicated. In this paper, we study the problem of visualizing a given clustering of overlapping clusters in bipartite graphs and the related problem of visualizing Boolean Matrix Factorizations. We conceptualize three different objectives that any good visualization should satisfy: (1) proximity of cluster elements, (2) large consecutive areas of elements from the same cluster, and (3) large uninterrupted areas in the visualization, regardless of the cluster membership. We provide objective functions that capture these goals and algorithms that optimize these objective functions. Interestingly, in experiments on real-world datasets, we find that the best trade-off between these competing goals is achieved by a novel heuristic, which locally aims to place rows and columns with similar cluster membership next to each other.