Update README.md

README.md

@@ -722,7 +722,7 @@ It is readily viewable below and we can see that the UMAP and Distance Geometry
 
 # Example 14: Connectivity, hierarchies and linkages
 
-In the `impetuous.clustering` module you will find several codes for assessing if distance matrices are connected at some distance or not. `connectivity` and `connectedness` are two methods for establishing the number of clusters in the binary Neighbour matrix. The Neighbour matrix is just the pairwise distance between the parts `i` and `j` of your system (<img src="https://render.githubusercontent.com/render/math?math=D_{ij}">) with an applied cutoff (<img src="https://render.githubusercontent.com/render/math?math=N_{ij}=D_{ij}\le\epsilon">) and is related to the adjacency matrix from graph theory by adding an identity matrix to the adjacency matrix (<img src="https://render.githubusercontent.com/render/math?math=A_{ij}=N_{ij} - I_{ij}">). The three boolean matrices that describe a system at some distance cutoff (<img src="https://render.githubusercontent.com/render/math?math=\epsilon">) are: the identity matrix (<img src="https://render.githubusercontent.com/render/math?math=I_{ij} = D_{ij}\equiv0 ">), the adjacency matrix (<img src="https://render.githubusercontent.com/render/math?math=A_{ij}= D_{ij}\le\epsilon - I_{ij}">) and the community matrix (<img src="https://render.githubusercontent.com/render/math?math=C_{ij}=D_{ij}>\epsilon">).
+In the `impetuous.clustering` module you will find several codes for assessing if distance matrices are connected at some distance or not. `connectivity` and `connectedness` are two methods for establishing the number of clusters in the binary Neighbour matrix. The Neighbour matrix is just the pairwise distance between the parts `i` and `j` of your system (<img src="https://render.githubusercontent.com/render/math?math=D_{ij}">) with an applied cutoff (<img src="https://render.githubusercontent.com/render/math?math=N_{ij}=D_{ij}\le\epsilon">) and is related to the adjacency matrix from graph theory by adding an identity matrix to the adjacency matrix (<img src="https://render.githubusercontent.com/render/math?math=A_{ij}=N_{ij} - I_{ij}">). The three boolean matrices that describe a system at some distance cutoff (<img src="https://render.githubusercontent.com/render/math?math=\epsilon">) are: the Identity matrix (<img src="https://render.githubusercontent.com/render/math?math=I_{ij} = D_{ij}\equiv0 ">), the Adjacency matrix (<img src="https://render.githubusercontent.com/render/math?math=A_{ij}= D_{ij}\le\epsilon - I_{ij}">) and the Community matrix (<img src="https://render.githubusercontent.com/render/math?math=C_{ij}=D_{ij}>\epsilon">). We note that summing the three matrices will return `1` for any `i,j` pair. 
 
 "Connection" algorithms, such as the two mentioned, evaluate every distance and add them to the same cluster if there is any true overlap for a specific distance cutoff. ["Link" algorithms](https://online.stat.psu.edu/stat555/node/85/) try to determine the number of clusters for all unique distances by reducing and ignoring some connections to already linked constituents of the system in accord with a chosen heuristic.