计算机科学
素描
数据流挖掘
光学(聚焦)
异常检测
数据挖掘
随机森林
数据流
点(几何)
参数统计
溪流
人工智能
算法
数学
统计
电信
计算机网络
物理
几何学
光学
作者
Sudipto Guha,Nina Mishra,Gourav Roy,Okke Schrijvers
摘要
In this paper we focus on the anomaly detection problem for dynamic data streams through the lens of random cut forests. We investigate a robust random cut data structure that can be used as a sketch or synopsis of the input stream. We provide a plausible definition of non-parametric anomalies based on the influence of an unseen point on the remainder of the data, i.e., the externality imposed by that point. We show how the sketch can be efficiently updated in a dynamic data stream. We demonstrate the viability of the algorithm on publicly available real data.
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