Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning

• URL: http://arxiv.org/abs/2507.20369v1
• Date: Sun, 27 Jul 2025 17:53:19 GMT
• Title: Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning
• Authors: Ahmed Shokry, Ayman Khalafallah,
• Abstract summary: We introduce a novel clustering approach based on meta-learning.<n>We employ a pre-trained Prior-Data Fitted Transformer Network (PFN) to perform clustering.<n>We show that our approach is superior to the state-of-the-art clustering techniques.
• Score: 3.4530027457862005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clustering is a core task in machine learning with wide-ranging applications in data mining and pattern recognition. However, its unsupervised nature makes it inherently challenging. Many existing clustering algorithms suffer from critical limitations: they often require careful parameter tuning, exhibit high computational complexity, lack interpretability, or yield suboptimal accuracy, especially when applied to large-scale datasets. In this paper, we introduce a novel clustering approach based on meta-learning. Our approach eliminates the need for parameter optimization while achieving accuracy that outperforms state-of-the-art clustering techniques. The proposed technique leverages a few pre-clustered samples to guide the clustering process for the entire dataset in a single forward pass. Specifically, we employ a pre-trained Prior-Data Fitted Transformer Network (PFN) to perform clustering. The algorithm computes attention between the pre-clustered samples and the unclustered samples, allowing it to infer cluster assignments for the entire dataset based on the learned relation. We theoretically and empirically demonstrate that, given just a few pre-clustered examples, the model can generalize to accurately cluster the rest of the dataset. Experiments on challenging benchmark datasets show that our approach can successfully cluster well-separated data without any pre-clustered samples, and significantly improves performance when a few clustered samples are provided. We show that our approach is superior to the state-of-the-art techniques. These results highlight the effectiveness and scalability of our approach, positioning it as a promising alternative to existing clustering techniques.

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