# Significance-Based Categorical Data Clustering

arXiv: https://arxiv.org/abs/2211.03956 · HTML: https://mlsanity.com/paper/2211.03956/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 68 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [To Cluster, or Not to Cluster: An Analysis of Clusterability Methods](https://mlsanity.com/paper/1808.08317/) (2018)
- [Unsupervised Heterogeneous Coupling Learning for Categorical Representation](https://mlsanity.com/paper/2007.10720/) (2020)
- [Statistical Significance for Hierarchical Clustering](https://mlsanity.com/paper/1411.5259/) (2014)
- [Statistical Significance of Clustering using Soft Thresholding](https://mlsanity.com/paper/1305.5879/) (2013)
- [Dimensionality Reduction for Categorical Data](https://mlsanity.com/paper/2112.00362/) (2021)
- [Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey](https://mlsanity.com/paper/2103.12019/) (2021)
- [U-statistical inference for hierarchical clustering](https://mlsanity.com/paper/1805.12179/) (2018)

## Built on by (most cited first, 3 of 3)

- [Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning](https://mlsanity.com/paper/2601.16491/) (2026)
- [Categorical Data Clustering via Value Order Estimated Distance Metric Learning](https://mlsanity.com/paper/2411.15189/) (2024)
- [Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering](https://mlsanity.com/paper/2603.04458/) (2026)

## Abstract (arXiv)

Although numerous algorithms have been proposed to solve the categorical data clustering problem, how to access the statistical significance of a set of categorical clusters remains unaddressed. To fulfill this void, we employ the likelihood ratio test to derive a test statistic that can serve as a significance-based objective function in categorical data clustering. Consequently, a new clustering algorithm is proposed in which the significance-based objective function is optimized via a Monte Carlo search procedure. As a by-product, we can further calculate an empirical p-value to assess the statistical significance of a set of clusters and develop an improved gap statistic for estimating the cluster number. Extensive experimental studies suggest that our method is able to achieve comparable performance to state-of-the-art categorical data clustering algorithms. Moreover, the effectiveness of such a significance-based formulation on statistical cluster validation and cluster number estimation is demonstrated through comprehensive empirical results.