Unsupervised anomaly detection in cloud computing is crucial for system security and efficiency. However, the challenges posed by large data volumes, low anomaly rates, and diverse anomaly patterns in time series within cloud computing scenarios make it difficult for previous methods to obtain consistent and reliable representations for distinguishing anomalies. To avoid the degradation of model representation ability caused by abnormal sparsity, we propose the Density Transformer, a novel reconstruction-based explicit association modeling model that can amplify the non-trivial correlation of abnormal points with adjacent time points. Specifically, we express the density association by calculating the kernel density estimate at each time point, and the series association by calculating the self-attention at each time point. Then, the model uses an adversarial training strategy to produce a more significant difference in "association discrepancy" between normal points and abnormal points, thereby ensuring robust results in anomaly detection. Our model has been rigorously evaluated on a comprehensive collection of 6 publicly available real-world datasets, and the Density Transformer can achieve up to 46% improvement in F1-score compared to existing methods.