How can I create a density reachable algorithm in DBSCAN

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  • Thread starter Arman777
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  • #1
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Main Question or Discussion Point

I am new to data science and I am trying to create an algorithm for the DBSCAN. I can label each point as core-border-noise. But after this step I am stuck. How can I seperate the density reachable cores and create clusters from these points ?
 

Answers and Replies

  • #2
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Here's a writeup on wikipedia perhaps it will give you a clue here:

https://en.wikipedia.org/wiki/DBSCAN
They do talk about how to decide if a point is part of a larger cluster or not:

Abstract Algorithm
The DBSCAN algorithm can be abstracted into the following steps:[5]

  1. Find the points in the ε (eps) neighborhood of every point, and identify the core points with more than minPts neighbors.
  2. Find the connected components of core points on the neighbor graph, ignoring all non-core points.
  3. Assign each non-core point to a nearby cluster if the cluster is an ε (eps) neighbor, otherwise assign it to noise.
A naive implementation of this requires storing the neighborhoods in step 1, thus requiring substantial memory. The original DBSCAN algorithm does not require this by performing these steps for one point at a time.
 

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