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Evaluation Of The Arrows Method For Classification Of Data

Author

Listed:
  • 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)

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.

Suggested Citation

  • Lanting Lu & Christine S. M. Currie, 2010. "Evaluation Of The Arrows Method For Classification Of Data," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 27(01), pages 121-142.
    • Handle: RePEc:wsi:apjorx:v:27:y:2010:i:01:n:s0217595910002600
    DOI: 10.1142/S0217595910002600
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    Cited by:

    1. L Lu & C S M Currie & R C H Cheng & J Ladbrook, 2011. "Classification analysis for simulation of the duration of machine breakdowns," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 760-767, April.

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