计算机应用 ›› 2012, Vol. 32 ›› Issue (04): 1070-1073.DOI: 10.3724/SP.J.1087.2012.01070

• 数据库技术 • 上一篇    下一篇

基于加权直觉模糊集合的聚类模型

昌燕,张仕斌   

  1. 成都信息工程学院 网络工程学院,成都 610225
  • 收稿日期:2011-10-19 修回日期:2011-11-24 发布日期:2012-04-20 出版日期:2012-04-01
  • 通讯作者: 昌燕
  • 作者简介:昌燕(1979-),女,内蒙古阿拉善左人,讲师,硕士研究生,主要研究方向:网络与信息安全、信息处理、模式识别;张仕斌(1971-),男,重庆丰都人,教授,主要研究方向:网络与信息安全、基于网络的计算机应用、智能控制。
  • 基金资助:
    四川省科技支撑计划项目;成都信息工程学院校选科研基金资助项目

Clustering model based on weighted intuitionistic fuzzy sets

CHANG Yan,ZHANG Shi-bin   

  1. School of Network Engineering, Chengdu University of Information Technology, Chengdu Sichuan 610225, China
  • Received:2011-10-19 Revised:2011-11-24 Online:2012-04-20 Published:2012-04-01
  • Contact: CHANG Yan

摘要: 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。

关键词: 直觉模糊集, 加权直觉模糊集合, 聚类中心, 等价样本, 隶属度矩阵, 密度函数

Abstract: 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.

Key words: intuitionistic fuzzy set, weighted intuitionistic fuzzy set, clustering center, equivalent sample, membership degree matrix, density function