Agglomerative hierarchical cluster tree
Z = linkage(X)
Z = linkage(X,method)
Z = linkage(X,method,metric)
Z = linkage(X,method,pdist_inputs)
Z = linkage(X,method,metric,'savememory',value)
Z = linkage(Y)
Z = linkage(Y,method)
Z = linkage(
returns
a matrix X
)Z
that encodes a tree of hierarchical
clusters of the rows of the real matrix X
.
Z = linkage(
creates
the tree using the specified X
,method
)method
, where method
describes
how to measure the distance between clusters.
Z = linkage(X,
performs
clustering using the distance measure method
,metric
)metric
to
compute distances between the rows of X
.
Z = linkage(X,
passes
parameters to the method
,pdist_inputs
)pdist
function,
which is the function that computes the distance between rows of X
.
Z = linkage(X,
uses
a memorysaving algorithm when method
,metric
,'savememory'
,value)value
is 'true'
,
and uses the standard algorithm when value
is 'false'
.
Z = linkage(
uses
a vector representation Y
)Y
of a distance matrix. Y
can
be a distance matrix as computed by pdist
,
or a more general dissimilarity matrix conforming to the output format
of pdist
.
Z = linkage(
creates
the tree using the specified Y
,method
)method
, where method
describes
how to measure the distance between clusters.

Matrix with two or more rows. The rows represent observations, the columns represent categories or dimensions.  

Algorithm for computing distance between clusters.
Default:  

Any distance metric that the
Default:  

A cell array of parameters accepted by the  

Either
When Default:  

A vector of distances with the same format as the output of
the


For example, suppose there are 30 initial nodes and at step
12 cluster 5 and cluster 7 are combined. Suppose their distance at
that time is 1.5. Then 
cluster
 clusterdata
 cophenet
 dendrogram
 inconsistent
 kmeans
 pdist
 silhouette
 squareform