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Many anomaly detection techniques have been proposed in literature. [1] [20] The performance of methods usually depend on the data sets. For example, some may be suited to detecting local outliers, while others global, and methods have little systematic advantages over another when compared across many data sets.
In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in 2000 for finding anomalous data points by measuring the local deviation of a given data point with respect to its neighbours.
The modified Thompson Tau test is used to find one outlier at a time (largest value of δ is removed if it is an outlier). Meaning, if a data point is found to be an outlier, it is removed from the data set and the test is applied again with a new average and rejection region. This process is continued until no outliers remain in a data set.
The term one-class classification (OCC) was coined by Moya & Hush (1996) [8] and many applications can be found in scientific literature, for example outlier detection, anomaly detection, novelty detection. A feature of OCC is that it uses only sample points from the assigned class, so that a representative sampling is not strictly required for ...
However, at 95% confidence, Q = 0.455 < 0.466 = Q table 0.167 is not considered an outlier. McBane [1] notes: Dixon provided related tests intended to search for more than one outlier, but they are much less frequently used than the r 10 or Q version that is intended to eliminate a single outlier.
Box-and-whisker plot with four mild outliers and one extreme outlier. In this chart, outliers are defined as mild above Q3 + 1.5 IQR and extreme above Q3 + 3 IQR. The interquartile range is often used to find outliers in data. Outliers here are defined as observations that fall below Q1 − 1.5 IQR or above Q3 + 1.5 IQR.
An example of isolating an anomalous point in a 2D Gaussian distribution. An example of random partitioning in a 2D dataset of normally distributed points is shown in the first figure for a non-anomalous point and in the second one for a point that is more likely to be an anomaly. It is apparent from the pictures how anomalies require fewer ...
Similarly, a standardized anomaly equals an anomaly divided by a standard deviation. [1] A group of anomalies can be analyzed spatially, as a map, or temporally, as a time series. It should not be confused for an isolated outlier. There are examples in atmospheric sciences and in geophysics.