tags: [AI/Tasks/Clustering, AI/Algorithms/DBSCAN]Copy

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