Spatial clustering seeks to divide spatial data into a set of meaningful subclasses, referred to as spatial clusters, so that spatial objects in the same cluster are similar and those in different clusters are unlike.
DBSCAN (Density-based spatial clustering of applications with noise) is a popular spatial clustering method used in a variety of applications. DBSCAN is a clustering method used in machine learning to distinguish high-density clusters from low-density clusters.
The goal of DBSCAN is to locate data point neighbourhoods that exceed a specific density criterion. The radius of the neighbourhood (eps) and the minimum number of neighbors/data points (minPts) within the radius of the neighbourhood define the density threshold.
Density-based spatial clustering of applications with noise is referred to as DBSCAN. It can detect arbitrary shaped clusters as well as clusters with noise (i.e. outliers).
The 'time' factor, which is handled as either another dimension or an attribute, is the main difference between spatial and ST clustering. The space in which events or attributes are clustered can be at least 2-dimensional (X,Y) or 3-dimensional (X,Y,Z).