Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning

TL;DR

This paper introduces a novel, parameter-free clustering method using a pre-trained transformer that leverages a few pre-clustered samples to accurately partition large datasets, outperforming existing techniques.

Contribution

The paper proposes a new clustering approach based on meta-learning with a pre-trained transformer, eliminating parameter tuning and improving accuracy with minimal pre-clustered samples.

Findings

Outperforms state-of-the-art clustering methods.

Works effectively with few pre-clustered samples.

Scales well to large datasets.

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.