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Comparison of hierarchical cluster analysis methods by cophenetic correlation
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Journal Article
Comparison of hierarchical cluster analysis methods by cophenetic correlation
2013
Overview
Purpose
This study proposes the best clustering method(s) for different distance measures under two different conditions using the cophenetic correlation coefficient.
Methods
In the first one, the data has multivariate standard normal distribution without outliers for
n
=
10
,
50
,
100
and the second one is with outliers (5%) for
n
=
10
,
50
,
100
. The proposed method is applied to simulated multivariate normal data
via
MATLAB software.
Results
According the results of simulation the Average (especially for
n
=
10
) and Centroid (especially for
n
=
50
and
n
=
100
) methods are recommended at both conditions.
Conclusions
This study hopes to contribute to literature for making better decisions on selection of appropriate cluster methods by using subgroup sizes, variable numbers, subgroup means and variances.
Publisher
Springer International Publishing,Springer Nature B.V
