Title: Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data (Revised) Show Chinese title

Title: 流数据的集成聚类与异常检测（INCAD）（修订版）

Abstract: Most current clustering based anomaly detection methods use scoring schema and thresholds to classify anomalies. These methods are often tailored to target specific data sets with "known" number of clusters. The paper provides a streaming clustering and anomaly detection algorithm that does not require strict arbitrary thresholds on the anomaly scores or knowledge of the number of clusters while performing probabilistic anomaly detection and clustering simultaneously. This ensures that the cluster formation is not impacted by the presence of anomalous data, thereby leading to more reliable definition of "normal vs abnormal" behavior. The motivations behind developing the INCAD model and the path that leads to the streaming model is discussed. Show Chinese abstract

Abstract: 大多数基于聚类的异常检测方法使用评分方案和阈值来分类异常。 这些方法通常针对具有“已知”聚类数量的具体数据集进行定制。 本文提出了一种流式聚类与异常检测算法，在执行概率异常检测和聚类的同时，不需要对异常分数设置严格的任意阈值，也不需要知道聚类的数量。 这确保了聚类形成不受异常数据存在的影响，从而更可靠地定义“正常与异常”行为。 文中讨论了开发INCAD模型的动机以及通往流式模型的发展路径。