# visClust: A visual clustering algorithm based on orthogonal projections

arXiv: https://arxiv.org/abs/2211.03894 · HTML: https://mlsanity.com/paper/2211.03894/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization](https://mlsanity.com/paper/2009.12829/) (2020)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [The Impact of Random Models on Clustering Similarity](https://mlsanity.com/paper/1701.06508/) (2017)
- [Adaptive Graph Auto-Encoder for General Data Clustering](https://mlsanity.com/paper/2002.08648/) (2020)
- [The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering](https://mlsanity.com/paper/1907.00680/) (2019)
- [On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems](https://mlsanity.com/paper/1901.07598/) (2019)

## Abstract (arXiv)

We present a novel clustering algorithm, visClust, that is based on lower dimensional data representations and visual interpretation. Thereto, we design a transformation that allows the data to be represented by a binary integer array enabling the use of image processing methods to select a partition. Qualitative and quantitative analyses measured in accuracy and an adjusted Rand-Index show that the algorithm performs well while requiring low runtime and RAM. We compare the results to 6 state-of-the-art algorithms with available code, confirming the quality of visClust by superior performance in most experiments. Moreover, the algorithm asks for just one obligatory input parameter while allowing optimization via optional parameters. The code is made available on GitHub and straightforward to use.