<p>Contrastive clustering, which leverages contrastive learning to enhance clustering performance, has made significant progress in recent years. The emergence of numerous diverse and valuable research outcomes highlights the necessity of systematically summarizing and analyzing these advancements. This paper conducts a comprehensive survey of contrastive clustering, offering a fresh insight into existing research. Firstly, the models and loss functions commonly used in contrastive clustering are summarized based on their prevalence, and an in-depth analysis is conducted on the reasons for their popularity. Secondly, existing research is systematically surveyed from the perspectives of data augmentation, positive and negative sample selection, and clustering representation, including the basic ideas, strengths, limitations and experimental performance of various methods. Thirdly, the applications of contrastive clustering in various domains are introduced, which helps readers broaden their horizons and gain insights into the application prospects and value of contrastive clustering. Finally, the current challenges and potential future research directions in contrastive clustering are discussed. This survey enables readers to quickly grasp the research landscape, developmental trajectory, and key achievements in this field, laying a solid foundation for further exploration.</p>