Unsupervised clustering aims at discovering the semantic categories of data
according to some distance measured in the representation space. However,
different categories often overlap with each other in the representation space
at the beginning of the learning process, which poses a significant challenge
for distance-based clustering in achieving good separation between different
categories. To this end, we propose Supporting Clustering with Contrastive
Learning (SCCL) -- a novel framework to leverage contrastive learning to
promote better separation. We assess the performance of SCCL on short text
clustering and show that SCCL significantly advances the state-of-the-art
results on most benchmark datasets with 3%-11% improvement on Accuracy and
4%-15% improvement on Normalized Mutual Information. Furthermore, our
quantitative analysis demonstrates the effectiveness of SCCL in leveraging the
strengths of both bottom-up instance discrimination and top-down clustering to
achieve better intra-cluster and inter-cluster distances when evaluated with
the ground truth cluster labels.

该论文提出了 SCCL 框架，使用对比学习来帮助无监督聚类，有效提升类别间距离，进而在短文本聚类中达到更好的结果。研究表明，SCCL 能够显著提高精度和归一化互信息，同时有效结合自下而上的实例判别和自上而下的聚类来实现更好的聚类效果。