Calculates normalized mutual information and adjusted mutual information. The value for both will be
a value bewteen 0 and 1 that measures how close the classification between the two clusters is.
A value closer to 1 means the labels are more similar across v1 and v2, and a value closer to 0 means
the labels are not as similar.
Usage
Mutual_Information(v1, v2)
Arguments
v1
vector containing first classification labels
v2
vector containing second classification labels
Value
Returns mutual information:
nmi
NMI value
ami
AMI value
Details
See these links for a more formal definition of AMI
and NMI.