提出两种新异常度量，更好识别高维数据中的异常点。
Outlyingness Scores with Cluster Catch Digraphs
- 用图结构结合密度分布设计异常度量，适应复杂簇形。
- 新方法在高维数据中表现最优，尤其对局部异常敏感。
- 适合处理高维、聚类不规则的数据，且抗共线性干扰。
本文提出基于聚类捕获有向图（CCDs）的两种新型异常度量：外向异常度量（OOS）与内向异常度量（IOS）。OOS衡量某点相对于其最近邻的异常程度，IOS则评估该点从所属簇中其他点接收的总体影响。二者均结合图、密度与分布方法，适用于高维数据中形状与强度各异的聚类。两者均可有效识别全局与局部异常点，且对数据共线性不变。此外，IOS对遮蔽问题具有鲁棒性。通过大量蒙特卡洛模拟，对比了两种度量与基于CCD的传统及前沿异常检测方法。结果表明，两种度量在人工与真实数据集上均显著优于现有基于CCD的方法，其中IOS在所有方法中表现最佳，尤其在高维场景下优势明显。
原文摘要 · Abstract (English)
This paper introduces two novel, outlyingness scores (OSs) based on Cluster Catch Digraphs (CCDs): Outbound Outlyingness Score (OOS) and Inbound Outlyingness Score (IOS). These scores enhance the interpretability of outlier detection results. Both OSs employ graph-, density-, and distribution-based techniques, tailored to high-dimensional data with varying cluster shapes and intensities. OOS evaluates the outlyingness of a point relative to its nearest neighbors, while IOS assesses the total ``influence" a point receives from others within its cluster. Both OSs effectively identify global and local outliers, invariant to data collinearity. Moreover, IOS is robust to the masking problems. With extensive Monte Carlo simulations, we compare the performance of both OSs with CCD-based, traditional, and state-of-the-art outlier detection methods. Both OSs exhibit substantial overall improvements over the CCD-based methods in both artificial and real-world data sets, particularly with IOS, which delivers the best overall performance among all the methods, especially in high-dimensional settings. Keywords: Outlier detection, Outlyingness score, Graph-based clustering, Cluster catch digraphs, High-dimensional data.
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