Density Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm locates regions of high density that are separated from one another by regions of low density. DBSCAN is a center based approach to clustering in which density is estimated for a particular point in the data set by counting the number of points within the specified radius, ɛ, of that point.
The center based approach to density allows us to classify a point as one of the three:
Core points: These points are in the interior of the dense region
Border points:These points are not the core points, but fall within the neighborhood of the core points
Noise points: A noise point is a point that is neither a core point nor a border point.
Core points: These points are in the interior of the dense region
Border points:These points are not the core points, but fall within the neighborhood of the core points
Noise points: A noise point is a point that is neither a core point nor a border point.
The formal definition of DBSCAN algorithm is illustrated below:
