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An application of the generalised K-means algorithm in decision-making processes

Author

Listed:
  • Hsin-Hung Wu
  • Jiunn-I Shieh
  • Anthony Y.H. Liao
  • Shih-Yen Lin

Abstract

A case study of applying the generalised K-means algorithm with different p values is provided to discuss the applicants' selection under a variety of criteria in an admission process. The properties of the generalised K-means algorithm are exploited in a decision-making process. When p is smaller and closer to zero, the results show the priorities are identical, which is to look for the applicants with even performance. In contrast, the most commonly used p values in K-means algorithm do not generate a systematic pattern. When p becomes larger and approaches ∞, the results show the priorities are difficult to tell, but the intention is to separate alternatives with a number of clusters, which is to look for the applicants with the greatest potential. Finally, in this case study, using smaller p values might provide stable priorities to select 21 applicants out of 36 participants.

Suggested Citation

  • Hsin-Hung Wu & Jiunn-I Shieh & Anthony Y.H. Liao & Shih-Yen Lin, 2008. "An application of the generalised K-means algorithm in decision-making processes," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 3(1/2), pages 19-35.
    • Handle: RePEc:ids:ijores:v:3:y:2008:i:1/2:p:19-35
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    Cited by:

    1. En-Chi Chang & Shian-Chang Huang & Hsin-Hung Wu, 2010. "Using K-means method and spectral clustering technique in an outfitter’s value analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(4), pages 807-815, June.

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