Driven by the increasing scale and high profile cybersecurity incidents related public data,
recent years we have witnessed a paradigm shift in understanding and defending against …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

This authoritative, expanded and updated second edition of Encyclopedia of Machine
Learning and Data Mining provides easy access to core information for those seeking entry …

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 …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and
Spatiotemporal DatabasesSince the publication of the first edition of Geographic Data …

Detecting outliers which are grossly different from or inconsistent with the remaining dataset
is a major challenge in real-world KDD applications. Existing outlier detection methods are …

Outlier detection has been a very important concept in the realm of data analysis. Recently,
several application domains have realized the direct mapping between outliers in data and …

Outlier detection is an important research problem in data mining that aims to discover
useful abnormal and irregular patterns hidden in large datasets. In this paper, we present a …

摘要离群点可分为全局离群点和局部离群点. 在很多情况下, 局部离群点的挖掘比全局离群点的
挖掘更有意义. 现有的基于局部离群度的离群点挖掘算法存在检测精度依赖于用户给定的参数 …