# Clustering Categorical Data: Soft Rounding k-modes

arXiv: https://arxiv.org/abs/2210.09640 · HTML: https://mlsanity.com/paper/2210.09640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 34 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 2 of 2)

- [Recent Developments in Boolean Matrix Factorization](https://mlsanity.com/paper/2012.03127/) (2020)
- [Johnson Coverage Hypothesis: Inapproximability of k-means and k-median in L_p metrics](https://mlsanity.com/paper/2111.10912/) (2021)

## Abstract (arXiv)

Over the last three decades, researchers have intensively explored various clustering tools for categorical data analysis. Despite the proposal of various clustering algorithms, the classical k-modes algorithm remains a popular choice for unsupervised learning of categorical data. Surprisingly, our first insight is that in a natural generative block model, the k-modes algorithm performs poorly for a large range of parameters. We remedy this issue by proposing a soft rounding variant of the k-modes algorithm (SoftModes) and theoretically prove that our variant addresses the drawbacks of the k-modes algorithm in the generative model. Finally, we empirically verify that SoftModes performs well on both synthetic and real-world datasets.