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add DOI badge in README
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Project.toml

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@@ -4,18 +4,17 @@ authors = ["AugustoCL <[email protected]>", "Elias Carvalho <eliascarvdev@
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version = "0.1.0"
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[deps]
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DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
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[compat]
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julia = "1.6"
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LinearAlgebra = "1.6"
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NearestNeighbors = "0.4.9"
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Statistics = "1.4.0"
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Tables = "1.6.0"
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julia = "1.6"
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[extras]
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

README.md

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[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://AugustoCL.github.io/ClusterAnalysis.jl/dev)
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[![Build Status](https://github.com/AugustoCL/ClusterAnalysis.jl/workflows/CI/badge.svg)](https://github.com/AugustoCL/ClusterAnalysis.jl/actions)
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[![Coverage](https://codecov.io/gh/AugustoCL/ClusterAnalysis.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/AugustoCL/ClusterAnalysis.jl)
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[![DOI](https://zenodo.org/badge/390558951.svg)](https://zenodo.org/badge/latestdoi/390558951)
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<img src="docs/src/plot_dbscan.png" width="70%">

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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<img src="../dbscan_video.gif" width="50%">
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```@raw html
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<img src="../dbscan_video.gif" width="75%">
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```
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## Benchmarking code
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```julia
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@benchmark m = dbscan(X, ϵ, min_pts)
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```
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```@raw html
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<img src="../benchmark_dbscan.png" width="70%">
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<img src="../benchmark_dbscan.png" width="75%">
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```
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**Scikit-Learn with C in backend**
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```@raw html
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<img src="../benchmark_sklearn_dbscan.png" width="70%">
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<img src="../benchmark_sklearn_dbscan.png" width="75%">
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```
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**R with C++ in backend**
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```@raw html
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<img src="../benchmark_R_dbscan.png" width="70%">
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<img src="../benchmark_R_dbscan.png" width="75%">
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```
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Machine settings used in benchmarking
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title="DBSCAN prediction\n(ϵ=$(ϵ), minPts=$(min_pts))")
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```
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```@raw html
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<img src="../plot_dbscan.png" width="70%">
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<img src="../plot_dbscan.png" width="75%">
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```
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## DBSCAN Struct

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