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Many anomaly detection techniques have been proposed in literature. [1] [22] 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.
The sixth chapter concerns outlier detection, comparing methods for identifying data points as outliers based on robust statistics with other widely used methods, and the final chapter concerns higher-dimensional location problems as well as time series analysis and problems of fitting an ellipsoid or covariance matrix to data.
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection [4] discusses the general pattern in various local outlier detection methods (including, e.g., LOF, a simplified version of LOF and LoOP) and abstracts from this into a general framework. This framework is then ...
Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence [clarify] on the values of the estimates. Therefore, it also can be interpreted as an outlier detection method. [1]
OPTICS-OF [5] is an outlier detection algorithm based on OPTICS. The main use is the extraction of outliers from an existing run of OPTICS at low cost compared to using a different outlier detection method. The better known version LOF is based on the same concepts.
The modified Thompson Tau test [citation needed] is a method used to determine if an outlier exists in a data set. The strength of this method lies in the fact that it takes into account a data set's standard deviation, average and provides a statistically determined rejection zone; thus providing an objective method to determine if a data ...
Whilst in one or two dimensions outlier detection using classical methods can be performed manually, with large data sets and in high dimensions the problem of masking can make identification of many outliers impossible. Robust methods automatically detect these observations, offering a serious advantage over classical methods when outliers are ...
In 2012, C. Dardis released the R package "Peirce" with various methodologies (Peirce's criterion and the Chauvenet method) with comparisons of outlier removals. Dardis and fellow contributor Simon Muller successfully implemented Thomsen's pseudo-code into a function called "findx".