update 'how to install' in docs

Author: AugustoCL

README.md

@@ -8,25 +8,24 @@ This package was <ins>**built from scratch**</ins>, entirely in [Julia Lang](jul
 
 This is mostly a learning experiment, but the package were also built and documented to be used by anyone, Plug-and-Play. Just input your data as an Array or a [Tables.jl](https://discourse.julialang.org/t/tables-jl-a-table-interface-for-everyone/14071) type (like [DataFrames.jl](https://dataframes.juliadata.org/stable/)), then start training your clusters algorithms and analyze your results. 
 
+Documentation: [https://augustocl.github.io/ClusterAnalysis.jl/](https://augustocl.github.io/ClusterAnalysis.jl/)
+
 ## Algorithms Implemented
 Currently we implemented two types of algorithms, a partitioned based ([K-Means](https://en.wikipedia.org/wiki/K-means_clustering)) and a spatial density based ([DBSCAN](https://en.wikipedia.org/wiki/DBSCAN)). 
 
+> Go check the `Algorithms Overview` Section that contains all the details of how it works the algorithm and also got the bibliography and papers used during the research and development of the code.
 
-## Algorithm's Overview
-Go check the algorithm's overview, in [documentation](https://augustocl.github.io/ClusterAnalysis.jl/algorithms/kmeans.html) that contains all the details of how it works the algorithm and also got the bibliography and papers used during the research and development of the code. 
-
-> It's a great introduction to the algorithm and a good resource to read along with the source code.
+>It's a great introduction to the algorithm and a good resource to read along with the source code.
 
 - [DBSCAN](https://augustocl.github.io/ClusterAnalysis.jl/algorithms/dbscan.html)
 - [K-Means](https://augustocl.github.io/ClusterAnalysis.jl/algorithms/kmeans.html)
 
 ## How to install ClusterAnalysis.jl
-Since this package it's not registred, it's necessary to use the github url as shown below.
 
 ```julia
 # press ] to enter in Pkg REPL mode.
 julia> ]
-pkg> add https://github.com/AugustoCL/ClusterAnalysis.jl
+pkg> add ClusterAnalysis
 ```  
 
 ## To-Do

docs/src/index.md

@@ -1,22 +1,47 @@
 # ClusterAnalysis.jl
 
-```@raw html
-<img src="plot_dbscan.png" width="70%">  
-```
-
-This package was **BUILT FROM SCRATCH**, entirely in [Julia Lang](https://julialang.org), and implements a few popular clustering algorithms like K-Means and DBSCAN. 
+This package was **Built from scratch**, entirely in [Julia Lang](https://julialang.org), and implements a few popular clustering algorithms like K-Means and DBSCAN. 
 
 This is mostly a learning experiment, but the package were also built and documented to be used by anyone, Plug-and-Play. Just input your data as an Array or a [Tables.jl](https://discourse.julialang.org/t/tables-jl-a-table-interface-for-everyone/14071) type (like [DataFrames.jl](https://dataframes.juliadata.org/stable/)), then start training your clusters algorithms and analyze your results. 
 
+
 ## Algorithms Implemented
 Currently we implemented two types of algorithms, a partitioned based ([K-Means](https://en.wikipedia.org/wiki/K-means_clustering)) and a spatial density based ([DBSCAN](https://en.wikipedia.org/wiki/DBSCAN)). 
 
-> Go check the `Algorithms` Section that contains all the details of how it works the algorithm and also got the bibliography and papers used during the research and development of the code. It's a great introduction to the algorithm and a good resource to read along with the source code.
+> Go check the `Algorithms` Section that contains all the details of how it works the algorithm and also got the bibliography and papers used during the research and development of the code.
+
+> It's a great introduction to the algorithm and a good resource to read along with the source code.
 
 ## How to install ClusterAnalysis.jl
-Since this package it's not registred, it's necessary to use the github url as shown below.
 
 ```julia
 # press ] to enter in Pkg REPL mode.
-pkg> add https://github.com/AugustoCL/ClusterAnalysis.jl
+pkg> add ClusterAnalysis
 ```  
+
+## A quick example
+
+```julia
+using ClusterAnalysis, DataFrames, CSV
+
+# load blob dataset from repo in github
+df = CSV.read("algo_overview/blob_data.csv", DataFrame, drop=[1]);
+X = df[:,1:2];
+y = df[:,end];
+
+# parameters of k-means
+ϵ = 0.35;
+min_pts = 10;
+
+# executing DBSCAN
+m = dbscan(X, ϵ, min_pts);
+
+# plot
+scatter(X[:,1], X[:,2], zcolor=m.labels, 
+        leg=false, 
+        title="DBSCAN prediction\n(ϵ=$(ϵ), minPts=$(min_pts))")
+```
+
+```@raw html
+<img src="plot_dbscan.png" width="70%">  
+```