- Description: Outliers often form small clusters or are isolated from main clusters.
8. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Purpose:
Finds anomalies based on density rather than explicit statistical assumptions. - Steps:
- Identify points in low-density regions as anomalies.
2. Local Outlier Factor (LOF)
LOF is a density-based anomaly detection method that identifies anomalies by comparing the local density of a point with that of its neighbors.
Steps:
- For each point, calculate the local density based on the distance to its k-nearest neighbors.
- Compute the LOF score, which measures the degree of isolation of a point relative to its neighbors.
- Points with a LOF score significantly greater than 1 are considered anomalies.