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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.
Find papers, benchmarks, datasets, and libraries for anomaly detection, a binary classification task that identifies unusual or unexpected patterns in data. Explore methods, results, and subtasks for unsupervised, one-class, and supervised anomaly detection.
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.
15 de nov. de 2021 · Anomaly detection is the process of finding unusual or unexpected patterns in data. IBM Developer offers a learning path to help you get started with anomaly detection API, a service that detects anomalies in time series data.
