Multi attribute gray clustering decision based on three parameter interval number
Gray clustering mainly studies the clustering decision with insufficient information. The interval of three parameters can effectively reduce the uncertainty of judgment. The attribute value of evaluation index is described by the interval of three parameters, and the typical gray clustering decision is expanded into three stages: data aggregation, gray cluster analysis and comprehensive decision. First, the three parameter interval number is integrated into a numerical value through the aggregation factor; then the numerical value is clustered by the whitening weight function; finally, the ranking result of the evaluation object is determined by comparing the decision vector. The application example shows the rationality and effectiveness of the clustering decision making method proposed in this paper, which provides a new idea for decision theory and application research.
2018 Chinese Control And Decision Conference (CCDC)