Density-based spatial clustering of applications with noise (DBSCAN)
Kloudfuse provides DBSCAN implementation of Outlier Detection.
In DBSCAN, the tolerance level, or eps
, determines the clustering radius of the neighborhood around each point. The eps
controls the sensitivity of outlier detection. A lower tolerance detects more subtle outliers, while a higher tolerance detects only the most significant deviations.
For an in-depth discussion of the DBSCAN algorithm, see these external resources:
-
DBSCAN Key Concepts and Parameters in DataCamp Tutorials
-
DBSCAN in Wikipedia