改进的聚类算法及在复杂大群体决策中的应用

Improved clustering algorithm and its application in complex huge group decision-making

针对现有FCM聚类算法中存在的局部极值和伸缩性较差等问题,提出了基于全部最小连通支配集算法(minimum connected donating set algorithm,MCDSA)的改进的聚类算法(minimum fuggy C-means,MF-CM)。用改进的聚类算法MFCM辅助复杂大群体决策(complex huge group-decision,CHGDS),定义了群体偏好矢量和群体一致性指标,提供了一种新的解决CHGDS中群体决策的理论和方法,并通过实验证明了该方法的有效性和稳定性。还提供了基于属性加权进化的群体一致性决策机制新思路。

To answer the questions of the existing FCM involving local limit value and bad scalability, the paper puts forward an improved clustering algorithm MFCM（minimum fuzzy C-means） basing on MCDSA（minimum connected donating set algorithm）. To help CHGDS （complex huge group-decision） using improved MFCM algorithm, the paper defines the preference vector and coherence indexes of the entire group and provides a new theory and method of group decision in CHGDS, it is validated by a test. Finally, the new idea of group coherence decision mechanism basing on attributes weighted is provided.