DBSCAN with Python: Benefits of DBSCAN

DBSCAN does this by measuring the distance each point is from one another, and if enough points are close enough together, then DBSCAN will classify it as a new cluster.

Beginners guide to Density-Based Spatial Clustering of Applications with Noise w/ Examples

KMeans has trouble with arbitrary cluster shapes. Image by Mikio Harman

Clustering is an unsupervised learning technique that finds patterns in data without being explicitly told what pattern to find.

DBSCAN does this by measuring the distance each point is from one another, and if enough points are close enough together, then DBSCAN will classify it as a new cluster.

As seen above, there are two distinct clusters in the Test Data. KMeans, another popular clustering technique, fails to accurately cluster this data because KMeans creates a linearly separable boundary between clusters when k=2.

DBSCAN instead defines clusters based on two parameters: Epsilon and Min_Points

Epsilon_ — The maximum distance a point can be from another point to be considered a neighbor._

Min_Points_ — The amount of points needed within the range of epsilon to be considered a cluster._

Benefits of DBSCAN

It requires minimal domain knowledge to determine the input parameters.

Other clustering algorithms like KMeans requires the user to know how many clusters exist in the data.

Instead of requiring how many clusters should be found, DBSCAN requires the user to input the maximum distance apart each point of data can be to be considered part of a cluster and how many data points it takes to form a cluster.

It discovers clusters of any shape.

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