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Using expert's rules as background knowledge in the ClusDM methodology

Author

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  • Valls, Aida
  • Batet, Montserrat
  • López, Eva M.

Abstract

In complex domains it is usually quite difficult to introduce context information. However, sometimes that information should be taken into account to make decisions, because it provides some relevant knowledge that cannot be expressed using an attribute-value representation. This is the case of the determination of risk of contamination of soils. In this paper, we propose to use conjunctive rules to introduce additional background knowledge to a MCDM sorting method called ClusDM. ClusDM is based on the aggregation of the data with unsupervised clustering techniques. The paper presents a new algorithm to incorporate rules to guide the clustering process in a semi-supervised way. The paper also describes how it works in the case sorting a set of possible contaminated soils, and compares the results obtained by ClusDM when rules are used or not.

Suggested Citation

  • Valls, Aida & Batet, Montserrat & López, Eva M., 2009. "Using expert's rules as background knowledge in the ClusDM methodology," European Journal of Operational Research, Elsevier, vol. 195(3), pages 864-875, June.
    • Handle: RePEc:eee:ejores:v:195:y:2009:i:3:p:864-875
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    Cited by:

    1. Meyer, Patrick & Olteanu, Alexandru-Liviu, 2013. "Formalizing and solving the problem of clustering in MCDA," European Journal of Operational Research, Elsevier, vol. 227(3), pages 494-502.
    2. Galo, Nadya Regina & Calache, Lucas Daniel Del Rosso & Carpinetti, Luiz Cesar Ribeiro, 2018. "A group decision approach for supplier categorization based on hesitant fuzzy and ELECTRE TRI," International Journal of Production Economics, Elsevier, vol. 202(C), pages 182-196.
    3. Jianguang Lu & Juan Tang & Bin Xing & Xianghong Tang, 2022. "Stochastic Approximate Algorithms for Uncertain Constrained K -Means Problem," Mathematics, MDPI, vol. 10(1), pages 1-14, January.

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