Impute missing assessments by opinion clustering in multi-criteria group decision making problems

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Multi-criteria group decision-making and evaluation (MCGDME) method typically aggregates information in evaluation tables. For various reasons, evaluation tables (decision matrix) often include missing data that highly affect correct decision-making and evaluation. Most existing imputation methods of missing data are based on statistical features which do not exist in an MCGDME setting. This paper proposes an imputation method of missing data (IMD) in evaluation tables. The IMD method measures the similarity betweent two evaluators’ mental models. Evaluators are then classed into several groups based on their similarities by using fuzzy clustering methods. Finally, missing data are imputated under the assumption that the imputated value of missing data does not change the previous clustering results. The proposed IMD method is implemented and tested in two numerical experiments.


Original languageEnglish
Title of host publicationIFSA/EUSFLAT 2009
Place of PublicationLisbon, Portugal
Publication statusPublished - Jul 2009
Event2009 World Congress International Fuzzy Systems Association - 2009 European Society for Fuzzy Logic and Technology, Lisbon, Portugal
Duration: 20 Jul 200924 Jul 2009


Conference2009 World Congress International Fuzzy Systems Association


  • decision making, missing data, multi-criteria evaluation, opinion clustering, aggregation

ID: 62957