Distance matrix

The distance matrix is used not just for visualization, but for learning algorithms too. You can think of them as a column of collections, where each cell contains the difference between the previous rows.

The supported distance functions are the following:

• Real distances

  • Euclidean()
  • Manhattan ()
  • Cosine ()

• Bitvector distances

  • Tanimoto ()
  • Dice ()
  • Bitvector cosine ()

• Distance vector (assuming you already have a distance vector, you can transform it to a distance matrix when there are row order changes or filtering)

• Molecule distances (from extensions)

The distance matrix feature can be used together with the hierarchical clustering, which also provides a node to view it; this is the main reason we introduced them in this chapter.

You can generate distances using the Distance Matrix Calculate node (just select the function, the numeric columns, and set the name. The chunk size is just for fine tuning larger tables), but you can also load that information with the Distance Matrix...