DBSCAN – Density-Based Spatial
Clustering of Applications with
Noise

DBSCAN
Density-based Clustering
locates regions of high
density that are separated from one another by
regions of low density.
Density = number of points within a specified
radius (Eps)
In Density-based Clustering methods, clusters
are grown by starting with some data points
and then including the neighboring points as
long as the neighborhood is sufficiently dense
These methods can find clusters of arbitrary
shape

DBSCAN
Clusters of arbitrary shape

DBSCAN
DBSCAN is a density-based algorithm.
A point is a
core point
if it has more than a
specified number of points (MinPts) within
Eps
These are points that are at the interior of a
cluster
A
border point
has fewer than MinPts within
Eps, but is in the neighborhood of a core
point. A border can fall within the
neighborhood of several core points

DBSCAN
A
noise point
is any point that is neither a
core point nor a border point.
Any two core points are close enough– within
a distance
Eps
of one another – are put in the
same cluster
Any border point that is close enough to a
core point is put in the same cluster as the
core point
Noise points are discarded

Border & Core
Core
Border
Outlier
= 1unit
MinPts = 5