基于加权直觉模糊集合的聚类模型

针对已有基于直觉模糊集的聚类方法的局限性，提出了一种基于加权直觉模糊集合的聚类模型——WIFSCM。在该模型中，提出了特定特征空间下的等价样本和加权直觉模糊集合的概念；并推导出基于等价样本和加权直觉模糊集合的直觉模糊聚类算法的目标函数，利用该目标函数推导出直觉模糊聚类中心迭代算法和隶属度矩阵迭代算法；定义了基于加权直觉模糊集合的密度函数，确定了初始聚类中心，减少了迭代次数。通过灰度图像分割实验，证明了该模型的有效性，同时与普通直觉模糊集FCM聚类算法(IFCM)相比，聚类速度提高近百倍。

Clustering model based on weighted intuitionistic fuzzy sets

Concerning the limitations of the existing clustering methods based on intuitionistic fuzzy sets,a clustering model called Weighted Intuitionistic Fuzzy Set Model(WIFSCM) was proposed based on weighted intuitionistic fuzzy sets.In this model,the concepts of equivalent sample and weighted intuitionistic fuzzy set were put forward in special feature space,and based on which the objective function of intuitionistic fuzzy clustering algorithm was proposed.Iterative algorithms of clustering center and matrix of membership degree were inferred from the objective function.The density function based on weighted intuitionistic fuzzy sets was defined,and initial clustering center was gotten to reduce iterative times.The experiment of gray image segmentation shows that WIFSCM is effective,and it is faster than IFCM algorithm nearly a hundred times.