摘要
将对象作顶点集,用直觉模糊数刻画对象间的相关性和不相关性表示成直觉模糊边;建立了半直觉模糊图模型。定义了半直觉模糊图的生成子图、度、路径、相关截图、序关系、最大生成树等概念。给出基于半直觉模糊图的聚类分析算法,分析了算法的复杂度。结合经典实例作了基于半直觉模糊图的聚类分析,结果显示基于半直觉模糊图的聚类分析算法复杂度低于一般直觉模糊聚类算法。高效实用且自动化程度高。
Objects are seen as vertex set. Intuitionistic fuzzy number is used to depict the correlation and irrelevance between objects, which are defined as intuitionistic fuzzy edge. This paper bulids a half intuitionistic fuzzy graph model.It gives the definitions about generated sub graph, degrees, path, the relevant screenshots, order relation and maximum spanning tree of a half intuitionistic fuzzy graph. It gives a clustering algorithm based on half intuition fuzzy graph, analyzes the complexity of the algorithm. Combined with an classic example, based on half intuitionistic fuzzy graph, it gives an example of the algorithm. The results show that the complexity of the clustering analysis algorithm based on half intuitionistic fuzzy graph is lower than the general intuition fuzzy clustering algorithm. It has high efficiency and automation.
作者
鱼先锋
YU Xianfeng(Institute of Mathematics and Computer Application, Shangluo University, Shangluo, Shaanxi 726000, China)
出处
《计算机工程与应用》
CSCD
北大核心
2016年第18期88-91,共4页
Computer Engineering and Applications
基金
陕西省教育厅专项科研计划项目(No.16JK1236)
商洛学院基金项目(No.15SKY001)
关键词
半直觉模糊图
最大生成树
聚类分析
half intuitionistic fuzzy graph
maximum spanning tree
clustering analysis
