Anomaly detection is a crucial technique in data mining and machine learning, used to identify outliers or rare events within a dataset. This page provides an overview of various anomaly detection techniques, along with examples and further reading.
Common Anomaly Detection Techniques
Statistical Methods
- Z-Score Analysis: Measures how far away a data point is from the mean.
- Interquartile Range (IQR): Identifies outliers based on the IQR, which is the range between the 25th and 75th percentiles.
Machine Learning-Based Methods
- Isolation Forest: A tree-based method that isolates anomalies instead of profiling normal data points.
- Local Outlier Factor (LOF): Measures the local deviation of density of a given data point with respect to its neighbors.
Clustering-Based Methods
- DBSCAN: A density-based clustering algorithm that can identify clusters of arbitrary shapes and detect anomalies.
- Autoencoders: Neural networks that are trained to reconstruct their input data, and anomalies are detected based on the reconstruction error.
Further Reading
For more in-depth information on anomaly detection techniques, check out our comprehensive guide on Anomaly Detection.
Anomaly Detection Visualization