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Copy file name to clipboardExpand all lines: docs/src/algorithms/dbscan.md
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@@ -5,7 +5,7 @@ In a certain way, the algorith try mimic the human hability of recognize groups
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**Figure 01** - Spatial Data example from the [First Paper](https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf) of DBSCAN
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```@raw html
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<img src="../spatial_data.png" width="70%">
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<img src="../spatial_data.png" width="75%">
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```
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This way we could identify points inside a density/cluster and points which isn't in any density cluster, called noises.
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**Figure 02** - Ilustration of DBSCAN from [this Paper](https://www.ccs.neu.edu/home/vip/teach/DMcourse/2_cluster_EM_mixt/notes_slides/revisitofrevisitDBSCAN.pdf)
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```@raw html
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<img src="../DBSCAN_ilustration.png" width="50%">
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<img src="../DBSCAN_ilustration.png" width="75%">
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```
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## Pseudocode
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We got inspired by this pseudo-code presented in the Paper "[DBSCAN Revisited](https://www.ccs.neu.edu/home/vip/teach/DMcourse/2_cluster_EM_mixt/notes_slides/revisitofrevisitDBSCAN.pdf)". Here, the reader could get a overview of the steps of the code.
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```@raw html
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<img src="../pseudo_code.png" width="50%">
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<img src="../pseudo_code.png" width="75%">
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```
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## A cool visualization that explain the algorithm
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Now, I need to share with the world this amazing website created by [Naftali Harris](https://www.naftaliharris.com/), from this detailed [post](https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/) about DBSCAN. I crop a gif from this interactive visualization because this way I hope would be easier for the reader connect all the information broughted above.
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