计算机与数字工程2024，Vol.52Issue(2)：307-314,320,9.DOI:10.3969/j.issn.1672-9722.2024.02.002
基于局部保留投影的稀疏中智聚类算法
Sparse Neutrosophic Clustering Algorithm Based on Local Preserving Projection
摘要
Abstract
Clustering algorithm is one of the important research topics in the field of machine learning.Traditional neutrosoph-ic clustering algorithms(such as FC-PFS algorithm)do not consider the local spatial structure,and the calculation of distance is af-fected by redundant features and cannot effectively process high-dimensional data set.A new sparse neutrosophic clustering algo-rithm(LPSNCM)based on local preserved projection and its optimization method are proposed in this paper.On the one hand,an orthogonal projection space with local structure information is generated by the local preserved projection method in the LPSNCM al-gorithm,on the other hand,the feature extraction method can reduce the number of features to obtain more effective features,thus enhancing the capability of FC-PFS algorithm to process high-dimensional data.The LPSNCM algorithm can also be regarded as a unified model of the two independent stages of spectral clustering.Experimental results on some benchmark datasets demonstrate the effectiveness of LPSNCM compared with FC-PFS and some of the latest methods.关键词
中智集/局部信息保留/基于投影的空间转化Key words
neutrosophic set/locality preserving projections/projection-based spatial transformation分类
信息技术与安全科学引用本文复制引用
张丹,马盈仓,杨小飞,邢志伟..基于局部保留投影的稀疏中智聚类算法[J].计算机与数字工程,2024,52(2):307-314,320,9.基金项目
国家自然科学基金项目(编号:61976130) （编号:61976130）
陕西省重点研发计划项目(编号:2018KW-021) （编号:2018KW-021）
陕西省自然科学基金项目(编号:2020JQ-923)资助. （编号:2020JQ-923）
