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Learn what anomaly detection is, why it is important and how it can be used in various industries. Explore different types of anomalies and methods to detect them, such as visualization, statistical tests and machine learning algorithms.
In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. [1]
Anomaly detection, sometimes called outlier detection, is a process of finding patterns or instances in a dataset that deviate significantly from the expected or “normal behavior.” The definition of both “normal” and anomalous data significantly varies depending on the context.
La detección de anomalías, o detección de valores atípicos, es la identificación de una observación, evento o punto de datos que se desvía de lo normal o esperado, haciéndolo incoherente con el resto del conjunto de datos.
La detección de anomalías examina puntos de datos específicos y detecta incidencias poco comunes que parecen sospechosas al ser diferentes de los patrones establecidos de comportamiento. La detección de anomalías no es algo nuevo, pero a medida que aumenta el volumen de datos el seguimiento manual ya no resulta práctico.
8 de nov. de 2023 · Learn how to use isolation forest, local outlier factor, robust covariance, one-class SVM and one-class SVM with SGD to detect anomalies in data. See the performance of these algorithms on a toy data set and watch a video tutorial.
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches.
