Density Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm locates regions of high density that are separated from one another by regions of low density. DBSCAN is a center based approach to clustering in which density is estimated for a particular point in the data set by counting the number of points within the specified radius,

**ɛ,**of that point.
The center based approach to density allows us to classify a point as one of the three:

**Core points:**These points are in the interior of the dense region**Border points:**These points are not the core points, but fall within the neighborhood of the core points**Noise points:**A noise point is a point that is neither a core point nor a border point.
The formal definition of DBSCAN algorithm is illustrated below:

- Eliminate noise points
- Perform clustering on remaining points
*current_cluster_label := 0*

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**for**all core points**do**
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**If**the core point has no*cluster_label***then***current_cluster_label := current_cluster_label +1*

Assign the current core point the

*current_cluster_label*
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**end if**
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**For**all points within the radius**do**
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**If**the point does not have a cluster_label**then**
Label the point with the

*current_cluster_label*
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**end if**
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**end for**
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