# Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning

arXiv: https://arxiv.org/abs/2601.16491 · HTML: https://mlsanity.com/paper/2601.16491/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 23 Jan 2026.

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

- [Unsupervised Heterogeneous Coupling Learning for Categorical Representation](https://mlsanity.com/paper/2007.10720/) (2020)
- [Significance-Based Categorical Data Clustering](https://mlsanity.com/paper/2211.03956/) (2022)
- [Clustering-based Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2511.08072/) (2025)

## Built on by (most cited first, 7 of 7)

- [Learning Order Forest for Qualitative-Attribute Data Clustering](https://mlsanity.com/paper/2603.03387/) (2026)
- [Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection](https://mlsanity.com/paper/2603.06757/) (2026)
- [Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers](https://mlsanity.com/paper/2603.12847/) (2026)
- [One-Shot Federated Clustering of Non-Independent Completely Distributed Data](https://mlsanity.com/paper/2601.17512/) (2026)
- [Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning](https://mlsanity.com/paper/2606.23199/) (2026)
- [Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery](https://mlsanity.com/paper/2607.06930/) (2026)
- [GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering](https://mlsanity.com/paper/2608.07881/) (2026)

## Abstract (arXiv)

Data set composed of categorical features is very common in big data analysis tasks. Since categorical features are usually with a limited number of qualitative possible values, the nested granular cluster effect is prevalent in the implicit discrete distance space of categorical data. That is, data objects frequently overlap in space or subspace to form small compact clusters, and similar small clusters often form larger clusters. However, the distance space cannot be well-defined like the Euclidean distance due to the qualitative categorical data values, which brings great challenges to the cluster analysis of categorical data. In view of this, we design a Multi-Granular Competitive Penalization Learning (MGCPL) algorithm to allow potential clusters to interactively tune themselves and converge in stages with different numbers of naturally compact clusters. To leverage MGCPL, we also propose a Cluster Aggregation strategy based on MGCPL Encoding (CAME) to first encode the data objects according to the learned multi-granular distributions, and then perform final clustering on the embeddings. It turns out that the proposed MGCPL-guided Categorical Data Clustering (MCDC) approach is competent in automatically exploring the nested distribution of multi-granular clusters and highly robust to categorical data sets from various domains. Benefiting from its linear time complexity, MCDC is scalable to large-scale data sets and promising in pre-partitioning data sets or compute nodes for boosting distributed computing. Extensive experiments with statistical evidence demonstrate its superiority compared to state-of-the-art counterparts on various real public data sets.