Lu, Lanting and Currie, Christine S.M.
Evaluation of the arrows method for classification of data
Asia-Pacific Journal of Operational Research, 27, (1), . (doi:10.1142/S0217595910002600).
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We evaluate the Arrows Classification Method (ACM) for grouping objects based on
the similarity of their data. This is a new method, which aims to achieve a balance
between the conflicting objectives of maximizing internal cohesion and external isolation
in the output groups. The method is widely applicable, especially in simulation input and
output modelling, and has previously been used for grouping machines on an assembly
line, based on data on time-to-repair; and hospital procedures, based on length-of-stay
data. The similarity of the data from a pair of objects is measured using the two-sample
Cram´er-von-Mises goodness of fit statistic, with bootstrapping employed to find the
significance or p-value of the calculated statistic. The p-values coming from the paired
comparisons serve as inputs to the ACM, and allow the objects to be classified such that
no pair of objects that are grouped together have significantly different data. In this
article, we give the technical details of the method and evaluate its use through testing
with specially generated samples. We will also demonstrate its practical application with
two real examples
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