摘要
在原有超图聚类算法的基础上提出一种基于超边相似性的改进的超图聚类算法,根据超边距离阈值形成超图模型并采用超图分割法对数据对象进行聚类,采用簇内奇异特征值进行评估聚类质量。
This paper proposes a improved algorithm of hypergraph clustering based on attribute similarity, on the basis of the original algorithm of hypergraph clustering. According to the threshold-edge distance form the hypergraph model and the hypergraph partitioning method to cluster the data object, using cluster-heads singular eigenvalue to evaluate the quality of clustering.
出处
《科技创新与生产力》
2015年第10期113-114,共2页
Sci-tech Innovation and Productivity
关键词
聚类
超图
超边距离阈值
clustering
hypergraph
super-edge distance threshold
