Titlebook: Outlier Analysis; Charu C. Aggarwal Book 20131st edition Springer Science+Business Media New York 2013 Data Analytics.Data Mining.Machine

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Supervised Outlier Detection, abnormalities in the data. In such scenarios, many of the anomalies found correspond to noise, and may not be of any interest to an analyst. It has been observed [284, 315, 440] in diverse applications such as system anomaly detection, financial fraud, and web robot detection that ..

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An Introduction to Outlier Analysis,An outlier is a data point which is significantly different from the remaining data.

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Proximity-Based Outlier Detection,Proximity-based techniques define a data point as an outlier, if its locality (or .) is sparsely populated. The proximity of a data point may be defined in a variety of ways, which are subtly different from one another, but are similar enough to merit a unified treatment within a single chapter.

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Outlier Detection in Graphs and Networks,Graphs represent one of the most powerful and general forms of data representation, which can express diverse data, ranging from multidimensional entity-relation graphs, the web, social networks, communication networks, and biological and chemical compounds.

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Charu C. AggarwalEach chapter contains key research content on the topic, case studies, extensive bibliographic notes and the future direction of research in this field.Covers applications for credit card fraud, netwo

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