电子科技2025，Vol.38Issue(7)：15-23,9.DOI:10.16180/j.cnki.issn1007-7820.2025.07.003
一种基于非线性表征和质心融合的模糊双中心聚类方法
A Fuzzy Double C-Means Clustering Method Based on Nonlinear Characterization and Centroid Fusion
摘要
Abstract
In view of the problem of high-precision clustering of non-negative incomplete data,an innovative fuzzy clustering method is proposed in this study.By introducing nonlinear function,case frequency regularization term and knowledge transfer to traditional latent factor model,the model representation ability and data filling accura-cy are improved,and a nonlinear latent factor model is formed.Combining sparse self-representation and centroid fu-sion term,the optimal cluster number is determined automatically while considering the global features,and a fuzzy bicentric clustering model is constructed.The experimental results on real data sets and pictures verify the effective-ness of the fuzzy bicentric clustering method based on nonlinear characterization and centroid fusion in dealing with the clustering problem of non-negative incomplete data.关键词
不完整数据/非线性函数/潜在因子分析/实例频率/质心融合/模糊聚类/稀疏自表示/知识迁移Key words
incomplete data/nonlinear function/latent factor analysis/instance frequency/centroid fusion/fuzzy clustering/sparse self-representation/knowledge transfer分类
信息技术与安全科学引用本文复制引用
赵丹,宋燕..一种基于非线性表征和质心融合的模糊双中心聚类方法[J].电子科技,2025,38(7):15-23,9.基金项目
国家自然科学基金(62073223) （62073223）
上海市自然科学基金(22ZR1443400)National Natural Science Foundation of China(62073223) （22ZR1443400）
Natural Science Foundation of Shanghai(22ZR1443400) （22ZR1443400）
