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  2. Isolation forest - Wikipedia

    en.wikipedia.org/wiki/Isolation_forest

    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.

  3. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    Anomaly detection is crucial in the petroleum industry for monitoring critical machinery. [20] Martí et al. used a novel segmentation algorithm to analyze sensor data for real-time anomaly detection. [20] This approach helps promptly identify and address any irregularities in sensor readings, ensuring the reliability and safety of petroleum ...

  4. Local outlier factor - Wikipedia

    en.wikipedia.org/wiki/Local_outlier_factor

    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.

  5. Change detection - Wikipedia

    en.wikipedia.org/wiki/Change_detection

    In statistical analysis, change detection or change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such ...

  6. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Each file represents a single experiment and contains a single anomaly. The dataset represents a multivariate time series collected from the sensors installed on the testbed. There are two markups for Outlier detection (point anomalies) and Changepoint detection (collective anomalies) problems 30+ files (v0.9) CSV Anomaly detection

  7. CUSUM - Wikipedia

    en.wikipedia.org/wiki/CUSUM

    As a means of assessing CUSUM's performance, Page defined the average run length (A.R.L.) metric; "the expected number of articles sampled before action is taken." He further wrote: [ 2 ] When the quality of the output is satisfactory the A.R.L. is a measure of the expense incurred by the scheme when it gives false alarms, i.e., Type I errors ...

  8. DBSCAN - Wikipedia

    en.wikipedia.org/wiki/DBSCAN

    DBSCAN is one of the most commonly used and cited clustering algorithms. [2] In 2014, the algorithm was awarded the Test of Time Award (an award given to algorithms which have received substantial attention in theory and practice) at the leading data mining conference, ACM SIGKDD. [3]

  9. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    Download QR code; Print/export ... this algorithm is local in time but not local in space. ... Time series anomaly detection [125] Text-to-Video model ...