EVALUATION OF THE ARROWS METHOD FOR CLASSIFICATION OF DATA

Lanting Lu () and Christine S. M. Currie ()
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Lanting Lu: Peninsula College of Medicine & Dentistry, University of Exeter, Veysey Building, Exeter, EX2 4SG, United Kingdom
Christine S. M. Currie: School of Mathematics, University of Southampton, Southampton, SO17 1BJ, United Kingdom

Asia-Pacific Journal of Operational Research (APJOR), 2010, vol. 27, issue 01, 121-142

Abstract: 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 Cramér-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.

Keywords: Arrows Classification Method; simulation; classification; goodness-of-fit statistics; bootstrapping (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0217595910002600

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