Density-Based Spatial Clustering (DBSCAN) with Python Code

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm It is a density-based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes.

It starts with an arbitrary starting point that has not been visited.

This point’s epsilon-neighborhood is retrieved, and if it contains sufficiently many points, a cluster is started. Then, a new unvisited point is retrieved and processed, leading to the discovery of a further cluster or noise. DBSCAN requires two parameters: epsilon (eps) and the minimum number of points required to form a cluster (minPts). If a point is found to be part of a cluster, its epsilon-neighborhood is also part of that cluster.

I implemented the pseudo code from DBSCAN wiki page:

 

Blue dots are actual data, red are noise and yellow are discovered clusters.
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simon
5 years ago

Thanks for sharing this post,
is very helpful article.

sani
5 years ago

hello
hello
This post is very nice and helpful

salah
5 years ago

thank you for sharing

salah
5 years ago

hello
very good aticle

djamila_st
5 years ago

good

chia
3 years ago

Thanks for all of the effort on this blog.

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