anchor.diff:      - [Silhouette coefficient](#Silhouette-coefficient)
       - [GapStatistics](#GapStatistics)
   
  +Clustering methods can be used to group elements of a huge data set based on their similarity. Elements sharing similar properties cluster together and can be reported as coherent group.
   For demonstration of several clustering methods, the classic iris data set is used, which consists of 150 records, 
   each of which contains four measurements and a species identifier. Since the species identifier occur several times 
   (Iris-virginica, Iris-versicolor, and Iris-setosa), the first step is to generate unique labels:
  
positive.code: #r "nuget: Plotly.NET.Interactive, 2.0.0-beta8"
  #r "nuget: FSharpAux, 1.0.0"
  #r "nuget: FSharp.Stats"
  #endif // IPYNB
  
  (**
  
negative.code: ## Density based clustering
  
  ### DBSCAN
  
  Further information can be found [here](https://fslab.org/content/tutorials/004_clustering_DBSCAN.html).
  
  *)
  //four dimensional clustering with sepal length, petal length, sepal width and petal width
  