Density-Based Spatial Clustering Application with Noise(DBSCAN) is a density-based clustering algorithm, which means density of items is used to assign items to clusters rather than a centroid or single point.
It requires two parameters:
minPts: the minimum number of data points that need to be clustered together for an area to be considered high-density.
eps: the distance used to determine if a data point is in the same area as other data points.
Pros and cons
DBSCAN is good at handling outliers
it can create arbitrarily shaped clusters
it’s good at handling oddly shaped data
It’s poor at handling lower density data, OPTICS is suggested as an alternative
it require fine-tuning initial parameters to work well