一种改进型专家模糊核聚类赋权方法研究  

Research on an Improved Expert Cluster Weighting Method Based on Fuzzy Kernel Clustering

作　　者：王泽洲 陈云翔[2] 项华春[2] WANG Ze-zhou;CHEN Yun-xiang;XIANG Hua-chun(No.93920 Unit of PLA,Hanzhong 723200 China;Equipment Management&UAV Engineering College,Air Force Engineering University,Xi'an 710051,China)

机构地区：[1]中国人民解放军93920部队,陕西汉中723200 [2]空军工程大学装备管理与无人机工程学院,陕西西安710051

出　　处：《中国管理科学》2021年第2期177-183,共7页Chinese Journal of Management Science

摘　　要：针对传统基于判断矩阵的专家模糊核聚类赋权方法,由于归一化条件的制约,导致离群点对聚类结果产生不良影响的问题,提出一种改进型模糊核聚类算法。该方法在聚类过程中,通过放宽归一化约束条件,削弱离群点对聚类结果的影响;并且针对传统基于信息熵与一致性系数线性耦合的聚类标准的局限性,提出一种基于偏差熵的赋权方法,依据专家对自身类别的聚类贡献度,确定专家权重,克服了传统方法的不足。算例表明,该方法可行、有效。In order to settle the matter that the outliers have an adverse impact on clustering results,which is caused by the restriction of traditional normalization conditions,an improved expert cluster weighting method based on fuzzy kernel clustering is proposed.By loosening the normalization constraints,the effects of outliers on the clustering results could be overcom,e.At the same time,considering the limitation of the traditional clustering standard based on the linear coupling of information entropy and consistency coefficient,a cluster weighting method based on deviation entropy is proposed.The expert weights’is determined based on the contribution of the expert clustering,which overcomes the deficiency of previous method.The example shows that the method is feasible and effective.

关 键 词：群决策 专家赋权 模糊核聚类 偏差熵 

分 类 号：C934[经济管理—管理学]