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  2. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    Anomaly detection is crucial in the petroleum industry for monitoring critical machinery. [18] Martí et al. used a novel segmentation algorithm to analyze sensor data for real-time anomaly detection. [18] This approach helps promptly identify and address any irregularities in sensor readings, ensuring the reliability and safety of petroleum ...

  3. 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.

  4. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining).

  5. 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.

  6. Wikipedia:Data mining Wikipedia - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Data_mining...

    Data mining involves six common classes of tasks: [1] Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation. Association rule learning (Dependency modelling) – Searches for relationships between variables. For example ...

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

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

    Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF: Anomaly detection: 2016 (possibly updated with new datasets and/or results) [331] Campos et al.

  8. Oracle Data Mining - Wikipedia

    en.wikipedia.org/wiki/Oracle_Data_Mining

    Oracle Data Mining (ODM) is an option of Oracle Database Enterprise Edition. It contains several data mining and data analysis algorithms for classification, prediction, regression, associations, feature selection, anomaly detection, feature extraction, and specialized analytics. It provides means for the creation, management and operational ...

  9. Anomaly Detection at Multiple Scales - Wikipedia

    en.wikipedia.org/wiki/Anomaly_Detection_at...

    Anomaly Detection at Multiple Scales, or ADAMS was a $35 million DARPA project designed to identify patterns and anomalies in very large data sets.