Some techniques for mining anomalies in data include statistical methods, proximity-based methods, and machine learning-based methods. Statistical methods involve identifying data points that deviate significantly from the expected pattern based on statistical properties. Proximity-based methods identify anomalies by comparing the distance or similarity between data points. Machine learning-based methods, on the other hand, involve training a model to recognize normal behavior and then identifying data points that deviate from this normal behavior.
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