Unsupervised anomaly detection stands as an important problem in machine learning, with applications in financial fraud prevention, network security and medical diagnostics. Existing unsupervised anomaly detection algorithms rarely perform well across different anomaly types, often excelling only under specific structural assumptions. This lack of robustness also becomes particularly evident under noisy settings. We propose Mean Shift Density Enhancement (MSDE), a fully unsupervised framework that detects anomalies through their geometric response to density-driven manifold evolution. MSDE is based on the principle that normal samples, being well supported by local density, remain stable under iterative density enhancement, whereas anomalous samples undergo large cumulative displacements as they are attracted toward nearby density modes. To operationalize this idea, MSDE employs a weighted mean-shift procedure with adaptive, sample-specific density weights derived from a UMAP-based fuzzy neighborhood graph. Anomaly scores are defined by the total displacement accumulated across a small number of mean-shift iterations. We evaluate MSDE on the ADBench benchmark, comprising forty six real-world tabular datasets, four realistic anomaly generation mechanisms, and six noise levels. Compared to 13 established unsupervised baselines, MSDE achieves consistently strong, balanced and robust performance for AUC-ROC, AUC-PR, and Precision@n, at several noise levels and on average over several types of anomalies. These results demonstrate that displacement-based scoring provides a robust alternative to the existing state-of-the-art for unsupervised anomaly detection.

翻译：无监督异常检测是机器学习中的一个重要问题，在金融欺诈预防、网络安全和医疗诊断等领域具有广泛应用。现有的无监督异常检测算法很少能在不同异常类型上均表现良好，通常仅在特定的结构假设下表现优异。这种鲁棒性的缺乏在噪声环境下表现得尤为明显。我们提出了均值漂移密度增强（MSDE），一种完全无监督的框架，通过异常点对密度驱动的流形演化的几何响应来检测异常。MSDE基于以下原理：正常样本由于得到局部密度的良好支撑，在迭代密度增强过程中保持稳定，而异常样本则会被吸引至附近的密度模态，从而经历较大的累积位移。为实现这一思想，MSDE采用了一种加权均值漂移过程，其自适应、样本特定的密度权重源自基于UMAP的模糊邻域图。异常分数由少量均值漂移迭代中累积的总位移量定义。我们在ADBench基准测试上评估了MSDE，该基准包含46个真实世界表格数据集、四种现实的异常生成机制和六个噪声级别。与13种成熟的无监督基线方法相比，MSDE在多个噪声级别上以及对多种异常类型的平均表现上，在AUC-ROC、AUC-PR和Precision@n指标上均取得了持续强劲、均衡且鲁棒的性能。这些结果表明，基于位移的评分方法为无监督异常检测提供了一种鲁棒的、可替代现有最先进技术的方法。