Anomaly Detection та виявлення аномалій

🎓 Methods

Statistical Methods

Z-score: Distance from the mean in standard deviations.

IQR: Interquartile Range for outliers.

Grubbs' Test: Statistical test for outliers.

Isolation Forest

Concept: Isolates anomalies through random splits.

Advantages: Effective, scalable.

Applications: High-dimensional data.

Autoencoders

Concept: Reconstruction error for anomaly detection.

Advantages: For complex patterns.

Applications: Deep learning for anomaly detection.

🔧 Advanced Methods

One-Class SVM

Concept: Finds the boundary of normal data.

Advantages: For non-linear patterns.

Applications: Novelty detection.

Local Outlier Factor (LOF)

Concept: Local density for outlier detection.

Advantages: For local outliers.

Applications: Clustering-based detection.

DBSCAN

Concept: Clustering with noise detection.

Advantages: Detects outliers as noise.

Applications: Density-based detection.

📚 Practical Examples

Example 1: Isolation Forest for fraud detection

Data: Prepare transaction data.

Isolation Forest: Train Isolation Forest.

Detection: Detect anomalous transactions.

Validation: Verify against known fraud cases.

Example 2: Autoencoder for anomaly detection

Training: Train autoencoder on normal data.

Threshold: Determine the threshold for reconstruction error.

Detection: Detect anomalies through high error.

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Anomaly Detection: identification of anomalies

Try it live

Everything above runs in your browser — open Anomaly Detection Point Cloud and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.