# Categorical data clustering: 25 years beyond K-modes

arXiv: https://arxiv.org/abs/2408.17244 · HTML: https://mlsanity.com/paper/2408.17244/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 221 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Clustering in graphs and hypergraphs with categorical edge labels](https://mlsanity.com/paper/1910.09943/) (2019)
- [Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge](https://mlsanity.com/paper/2003.12127/) (2020)
- [Mixture of Latent Trait Analyzers for Model-Based Clustering of Categorical Data](https://mlsanity.com/paper/1301.2167/) (2013)
- [Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space](https://mlsanity.com/paper/2209.15567/) (2022)
- [On clustering levels of a hierarchical categorical risk factor](https://mlsanity.com/paper/2304.09046/) (2023)
- [A Hash-based Co-Clustering Algorithm for Categorical Data](https://mlsanity.com/paper/1407.7753/) (2014)
- [Clustering categorical data via ensembling dissimilarity matrices](https://mlsanity.com/paper/1506.07930/) (2015)
- [Detecting organized eCommerce fraud using scalable categorical clustering](https://mlsanity.com/paper/1910.04514/) (2019)
- [Hierarchical clustering of bipartite data sets based on the statistical significance of coincidences](https://mlsanity.com/paper/2004.14764/) (2020)
- [Parameterized Complexity of Categorical Clustering with Size Constraints](https://mlsanity.com/paper/2104.07974/) (2021)
- [Parameterized Complexity of Feature Selection for Categorical Data Clustering](https://mlsanity.com/paper/2105.03753/) (2021)
- [On Clustering Categories of Categorical Predictors in Generalized Linear Models](https://mlsanity.com/paper/2110.10059/) (2021)
- [Mutual Information Scoring: Increasing Interpretability in Categorical Clustering Tasks with Applications to Child Welfare Data](https://mlsanity.com/paper/2208.01802/) (2022)
- [Spectral Clustering of Categorical and Mixed-type Data via Extra Graph Nodes](https://mlsanity.com/paper/2403.05669/) (2024)

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

- [Data clustering: a fundamental method in data science and management](https://mlsanity.com/paper/2412.18760/) (2024)
- [Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models](https://mlsanity.com/paper/2601.01162/) (2026)

## Abstract (arXiv)

The clustering of categorical data is a common and important task in computer science, offering profound implications across a spectrum of applications. Unlike purely numerical data, categorical data often lack inherent ordering as in nominal data, or have varying levels of order as in ordinal data, thus requiring specialized methodologies for efficient organization and analysis. This review provides a comprehensive synthesis of categorical data clustering in the past twenty-five years, starting from the introduction of K-modes. It elucidates the pivotal role of categorical data clustering in diverse fields such as health sciences, natural sciences, social sciences, education, engineering and economics. Practical comparisons are conducted for algorithms having public implementations, highlighting distinguishing clustering methodologies and revealing the performance of recent algorithms on several benchmark categorical datasets. Finally, challenges and opportunities in the field are discussed.