TabClustPFN: A Prior-Fitted Network for Tabular Data Clustering

• URL: http://arxiv.org/abs/2601.21656v2
• Date: Fri, 30 Jan 2026 07:18:19 GMT
• Title: TabClustPFN: A Prior-Fitted Network for Tabular Data Clustering
• Authors: Tianqi Zhao, Guanyang Wang, Yan Shuo Tan, Qiong Zhang,
• Abstract summary: We introduce TabClustPFN, a prior-fitted network for data clustering.<n>It performs amortized Bayesian inference over both cluster assignments and cluster cardinality.<n>It outperforms classical, deep, and amortized clustering baselines.
• Score: 11.86976498650059
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clustering tabular data is a fundamental yet challenging problem due to heterogeneous feature types, diverse data-generating mechanisms, and the absence of transferable inductive biases across datasets. Prior-fitted networks (PFNs) have recently demonstrated strong generalization in supervised tabular learning by amortizing Bayesian inference under a broad synthetic prior. Extending this paradigm to clustering is nontrivial: clustering is unsupervised, admits a combinatorial and permutation-invariant output space, and requires inferring the number of clusters. We introduce TabClustPFN, a prior-fitted network for tabular data clustering that performs amortized Bayesian inference over both cluster assignments and cluster cardinality. Pretrained on synthetic datasets drawn from a flexible clustering prior, TabClustPFN clusters unseen datasets in a single forward pass, without dataset-specific retraining or hyperparameter tuning. The model naturally handles heterogeneous numerical and categorical features and adapts to a wide range of clustering structures. Experiments on synthetic data and curated real-world tabular benchmarks show that TabClustPFN outperforms classical, deep, and amortized clustering baselines, while exhibiting strong robustness in out-of-the-box exploratory settings. Code is available at https://github.com/Tianqi-Zhao/TabClustPFN.

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