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ELKI is an open-source Java data mining toolkit that contains several anomaly detection algorithms, as well as index acceleration for them. PyOD is an open-source Python library developed specifically for anomaly detection. [52] scikit-learn is an open-source Python library that contains some algorithms for unsupervised anomaly detection.
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.
Anomaly detection: 2020 (continually updated) [329] [330] Iurii D. Katser and Vyacheslav O. Kozitsin On the Evaluation of Unsupervised Outlier Detection: Measures, Datasets, and an Empirical Study Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature.
Version 0.7.5 (February 2019) adds additional clustering algorithms, anomaly detection algorithms, evaluation measures, and indexing structures. [ 18 ] Version 0.8 (October 2022) adds automatic index creation, garbage collection, and incremental priority search, as well as many more algorithms such as BIRCH .
Isolation Forest is an algorithm for data anomaly detection using binary trees.It was developed by Fei Tony Liu in 2008. [1] It has a linear time complexity and a low memory use, which works well for high-volume data.
Autoencoders are applied to many problems, including facial recognition, [5] feature detection, [6] anomaly detection, and learning the meaning of words. [7] [8] In terms of data synthesis, autoencoders can also be used to randomly generate new data that is similar to the input (training) data. [6]
A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.
In machine learning, one-class classification (OCC), also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by primarily learning from a training set containing only the objects of that class, [1] although there exist variants of one-class classifiers where counter-examples are used to further refine the classification boundary.