DBSCAN(Density Clustering)

Density Clustering (DBSCAN) groups together points that are closely packed in space and marks points in low-density regions as outliers. It does not require the number of clusters to be specified in advance.

The algorithm discovers clusters automatically and labels isolated points as noise.

• DBSCAN is the only clustering algorithm in this package that can automatically detect outliers and determine the number of clusters on its own.
• NeighborhoodDistance, Estimated potential neighbor distance
• MinNeighbors, Estimated potential neighbor count but if there is no estimation, it can be estimated automatically with “Auto” option.

Use Cases

• Noise detection: Identify irregular or outlier geometries in a set of building elements.
• Unknown number of clusters: When you do not know how many groups exist in your spatial data.