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ML | DBSCAN reachability and connectivity

Prerequisite : DBSCAN Clustering in ML

Density-based clustering algorithm has played a vital role in finding nonlinear shapes structure based on the density. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is the most widely used density-based algorithm. It uses the concept of density reachability and density connectivity.



Consider a set of points in some space to be clustered using DBSCAN clustering. Let ε be the radius of a neighborhood with respect to some point and core objects are the objects whose ε-neighborhood contains at least MinPts number of objects.

Reachability –

Here density reachability is not symmetric. As q is not a core point thus qn-1 is not directly density-reachable from q, so object p is not density-reachable from object q.

Connectivity –

 
Based on the above two concepts reachability and connectivity we can define the cluster and noise points.

Cluster:
A cluster C w.r.t. ε and MinPts is a non empty subset of D (the whole set of objects or instances) satisfying –

Noise:
Objects which are not directly density-reachable from at least one core object are known as Noise points.

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