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Anomaly Detection

Simple Definition for Beginners: Anomaly detection is the process of identifying unusual patterns or behaviors in data that do not conform to expected norms. Common Use Example: In credit card fraud detection, anomaly detection systems identify transactions that are unusual compared to a customer's typical spending behavior, such as a sudden large purchase in a foreign country. Technical Definition for Professionals: Anomaly detection involves identifying data points, events, or observations that deviate significantly from the expected pattern of a given dataset. It is used in various fields such as fraud detection, network security, and fault detection. Key aspects and methods of anomaly detection include: · Statistical Methods: Using statistical models to identify anomalies based on deviation from a statistical norm or distribution. o Z-score: Measures how many standard deviations a data point is from the mean. o Grubbs' Test: Identifies outliers in a dataset assumed to be normally distributed. · Machine Learning Approaches: Utilizing algorithms to learn from data and detect anomalies. o Supervised Learning: Requires labeled training data with known anomalies. o Unsupervised Learning: Identifies anomalies in unlabeled data, commonly using clustering and density estimation. o Semi-supervised Learning: Combines labeled normal data with unlabeled data for anomaly detection. · Proximity-Based Methods: Detecting anomalies based on their distance from other data points. o K-Nearest Neighbors (KNN): Identifies anomalies based on the distance to the nearest neighbors. o Local Outlier Factor (LOF): Measures the local density deviation of a data point compared to its neighbors. · Information-Theoretic Methods: Using information theory measures such as entropy to detect anomalies. · Spectral Methods: Identifying anomalies using eigenvalue analysis and principal component analysis (PCA). Anomaly detection systems are essential for identifying and responding to unexpected events, ensuring the integrity of systems, data, and processes.

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