njit-etd2003-081 - New Jersey Institute of Technology

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4.2.11 Cluster Analysis
The same data used for PCA in the previous section were also used for cluster analysis
in separating the normal and COPD subjects and severity classification. The data set
was entered into the Matlab clustering program. The program executed according to the
following syntax:
T = clusterdata(X,cutoff)
where:
X was a matrix of size m by n, interpreted as m observations of n variables.
cutoff was a threshold value that determines how the cluster function creates clusters.
The value of cutoff determined how clusterdata interpreted it.
If 0 < cutoff < 1, cutoff was interpreted as the threshold for the inconsistency
coefficient. The inconsistency coefficient quantified the degree of difference between
objects in the hierarchical cluster tree. If the inconsistency coefficient of a link was
greater than the threshold, the cluster function would use the link as a boundary for a
cluster grouping.
If cutoff >= 1 cutoff was interpreted as the maximum number of clusters to retain in the
hierarchical tree.
The output, T, was a vector of size m that identifies, by number, the cluster in
which each object was grouped. The program used the clusterdata function to compute
the distances between items in the dataset and create a hierarchical cluster tree from the
dataset. Finally, the clusterdata function grouped the items in the dataset into the
number of cluster specified by the value "cutoff'.