AMI: Adjusted Mutual Information
In aricode: Efficient Computations of Standard Clustering Comparison Measures
AMI R Documentation
Adjusted Mutual Information
Description
A function to compute the adjusted mutual information between two classifications
Usage
AMI(c1, c2)
Arguments
c1
a vector containing the labels of the first classification. Must be a vector of characters, integers, numerics, or a factor, but not a list.
c2
a vector containing the labels of the second classification.
Value
a scalar with the adjusted rand index.
See Also
ARI, RI, NID, NVI, NMI, clustComp
Examples
data(iris)
cl <- cutree(hclust(dist(iris[,-5])), 4)
AMI(cl,iris$Species)
aricode documentation built on Oct. 20, 2023, 5:07 p.m.
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| AMI | R Documentation |
Adjusted Mutual Information
Description
A function to compute the adjusted mutual information between two classifications
Usage
AMI(c1, c2)
Arguments
c1 |
a vector containing the labels of the first classification. Must be a vector of characters, integers, numerics, or a factor, but not a list. |
c2 |
a vector containing the labels of the second classification. |
Value
a scalar with the adjusted rand index.
See Also
ARI, RI, NID, NVI, NMI, clustComp
Examples
data(iris)
cl <- cutree(hclust(dist(iris[,-5])), 4)
AMI(cl,iris$Species)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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