群体分类偏好下的双重语言信息融合聚类方法

研究一类基于双重信息融合的群体聚类方法。依据偏好信息下专家意见相似关系挖掘群体分类偏好信息；考虑专家决策依据向量的相似程度，设计一致性和非一致性测度指标，以表征双重维度下群体聚类的一致及差异度；以群体聚类结果差异最小为目标构建规划模型，测算属性权重，并以编网聚类的思想给出聚类结果。算例研究验证了所提出方法的科学性和合理性。

Clustering method based on dual linguistic information fusion considering group classification preference

A clustering method based on the dual linguistic information fusion is proposed to solve the conflict of clustering results caused by different sorts of information. Specifically, the experts’ similarities are calculated according to preference information, which can be employed to obtain the prior group classification preference. With the similarity vector of decision support information, the consistency and inconsistency indexes are introduced to present the extent of uniformity and difference of group clustering results from dual-dimension calculation. With the objective of minimizing the inconsistency measures of group clustering results, a programming model is constructed to calculate the attribute weights. Furthermore, the netting clustering method is utilized to determine the clustering results. Finally, a case study is conducted to illurstrate the rationality of the proposed method.