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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High-Dimensional Outlier Detection: The Subspace Method,nsionality, many of the conventional outlier detection methods do not work very effectively. This is an artifact of the well known .. In high-dimensional space, the data becomes sparse, and the true outliers become masked by the noise effects of multiple dimensions, when analyzed in ..

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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 ..

Discrete

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Time Series and Multidimensional Streaming Outlier Detection,s. In many cases, the detection of unusual events needs to be performed in a time-critical manner. This is also referred to as .. In some cases, the stream may not be available in real time, but may be available at a later stage for offline processing. In such cases, the advantage of hind-sight can

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Outlier Detection in Discrete Sequences, are discrete. In other words, the values at the time stamps are categorical. Such scenarios arise quite commonly in a variety of system diagnosis, intrusion detection and biological data applications. It is to be noted that in some domains such as intrusion detection and system diagnosis, the discr

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