Cophenetic correlation coefficient
c = cophenet(Z,Y)
[c,d] = cophenet(Z,Y)
c = cophenet(Z,Y) computes
the cophenetic correlation coefficient for the hierarchical cluster
tree represented by
the output of the
the distances or dissimilarities used to construct
as output by the
a matrix of size (m–1)-by-3, with distance
information in the third column.
Y is a vector
of size m*(m–1)/2.
[c,d] = cophenet(Z,Y) returns the cophenetic
d in the same lower triangular distance
vector format as
The cophenetic correlation for a cluster tree is defined as the linear correlation coefficient between the cophenetic distances obtained from the tree, and the original distances (or dissimilarities) used to construct the tree. Thus, it is a measure of how faithfully the tree represents the dissimilarities among observations.
The cophenetic distance between two observations is represented in a dendrogram by the height of the link at which those two observations are first joined. That height is the distance between the two subclusters that are merged by that link.
The output value,
c, is the cophenetic correlation
coefficient. The magnitude of this value should be very close to 1
for a high-quality solution. This measure can be used to compare alternative
cluster solutions obtained using different algorithms.
The cophenetic correlation between
Yij is the distance between objects i and j in
Zij is the cophenetic distance between objects i and j, from
y and z are the average of
X = [rand(10,3); rand(10,3)+1; rand(10,3)+2]; Y = pdist(X); Z = linkage(Y,'average'); % Compute Spearman's rank correlation between the % dissimilarities and the cophenetic distances [c,D] = cophenet(Z,Y); r = corr(Y',D','type','spearman') r = 0.8279