Agglomerative cluster analysis
75
15
Agglomerative cluster analysis
Hierarchical agglomerative cluster analysis is selected by choosing
Clustering|Agglomerative from the drop down menu. The basic process is that all
the samples (columns) start as separate groups that are joined together one at a
time until a single group is formed. At each iteration, a number of different
procedures can be used to determine which groups should be joined. The
following methods for linking groups are available:
Ward s
75
Single linkage
75
.
Complete linkage
75
.
Average linkage.
76
McQuitty s
76
Gower s.
76
Centroid.
76
In the Setup For Cluster box, 
CAP
 then offers a choice of 9 distance measures:
Euclidean
91
, 
Geodesic
92
, 
Whittakers
93
, 
Ave. Distance
92
, 
Manhattan
92
,
Canberra
93
, 
Chord
92
,  
Mean Character Difference (Czekanowski)
91
 or 
Bray 
Curtis
94
 distance.
Once a measure has been selected the cluster analysis will immediately be run.
Output is presented under a number of tabbed components that can each be
viewed by clicking on the tab. These are described in turn below.
Cluster summary
77
Cluster groups
77
Dendrogram   CLUSTER ANALYSIS
77
15.1
Ward's
Also termed minimum variance or error sums of squares clustering. At each
iteration, all possible pairs of groups are compared and the two groups chosen for
fusion are those which will produce a group with the lowest variance.
15.2
Single linkage
Also termed minimum or nearest neighbour method. At each iteration, the
clusters are compared in terms of the similarity of their most similar samples
(columns) and the two clusters that hold the most similar samples are fused.
15.3
Complete linkage
This is also called furthest neighbour sorting. At each step, the clusters are
compared in terms of the similarity of their least similar members and the two
clusters that are most similar are fused.
Copyright 2004, PISCES CONSERVATION LTD





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