摘要
针对传统谱聚类算法在非平衡数据集上聚类效果不理想的问题,提出了一种平衡化谱聚类算法,该算法在传统谱聚类目标函数的基础上加入了对聚类隶属度矩阵的近似正交约束,从而得到新的聚类目标函数.实验结果表明,新算法可以缓解传统谱聚类产生的均匀效应,提升了在非平衡数据集上的聚类纯度.
Aiming at the problem that the traditional spectral clustering algorithm has poor clustering effect on unbalanced data sets, a balanced spectral clustering algorithm is proposed.The algorithm adds an approximate orthogonal constraint to the clustering membership matrix on the basis of the traditional spectral clustering objective function to obtain a new clustering objective function.The experimental results show that the new algorithm can alleviate the uniform effect of traditional spectral clustering and improve the clustering purity on unbalanced datasets.
作者
苏扬
胡恩良
SU Yang;HU Enliang(School of Mathematics,Yunnan Normal University,Kunming 650500,China)
出处
《云南师范大学学报(自然科学版)》
2023年第1期21-25,共5页
Journal of Yunnan Normal University:Natural Sciences Edition
基金
国家自然科学基金资助项目(62266055)。
关键词
谱聚类
类不平衡问题
正交约束
高斯-牛顿法
聚类纯度
Spectral clustering
Class imbalance problem
Orthogonality
Gauss-Newton methods
Cluster purity