Contrastive self-supervised learning has significantly improved the performance of deep learning methods, such as representation learning and clustering. However, due to their dependence on data augmentation, these methods are mostly utilized in computer vision. In this paper, we investigate the adaptation of the recent contrastive clustering approach in the case of tabular data. Our experiments show that it outperforms typical clustering methods applicable to tabular data in most cases. Our findings affirm the potential adaptability of successful contrastive clustering techniques from other fields, such as image processing, to the realm of tabular data.