Titlebook: Outlier Analysis; Charu C. Aggarwal Textbook 2017Latest edition Springer International Publishing AG 2017 Outlier Analysis.Anomaly detecti

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Outlier Ensembles, algorithms or . to create a unified output. The basic idea of the approach is that some algorithms will do well on a particular subset of points whereas other algorithms will do better on other subsets of points. However, the ensemble combination is often able to perform more robustly across the bo

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Supervised Outlier Detection,bnormalities in the data. In such scenarios, many of the anomalies found correspond to noise or other uninteresting phenomena. It has been observed [338, 374, 531] in diverse applications such as system anomaly detection, financial fraud, and Web robot detection that .. In such cases, an unsupervise

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Outlier Detection in Categorical, Text, and Mixed Attribute Data,ication because categorical attributes are ubiquitous in real-world data. For example, although demographic data may contain quantitative attributes such as the age, most other attributes such as gender, race, and ZIP code are categorical. Data collected from surveys may often contain responses to m

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Spatial Outlier Detection,temporal attributes to occur in various combinations of behavioral and contextual attributes. Such data is also referred to as . data. For example, in some applications, such as hurricane tracking, the contextual attributes are both spatial and temporal.

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Applications of Outlier Analysis,etection, Web analytics, and medical diagnosis. The applications of outlier analysis are so diverse that it is impossible to exhaustively cover all possibilities in a single chapter. Therefore, the goal of this chapter is to cover many problem domains at a higher level and show how they map to the v

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