辽宁大学学报（自然科学版）2023，Vol.50Issue(4)：289-301,13.
K近邻空间密度分布的模糊聚类算法
Fuzzy Clustering Algorithm for K-Nearest Neighbors Spatial Density Distribution
摘要
Abstract
Clustering is an essential tool in data mining research and applications,but incomplete data poses challenges to existing clustering algorithms.Aiming at the uncertainty problem caused by interpolation in incomplete data clustering,a fuzzy clustering algorithm for K-nearest neighbors spatial density distribution is proposed.Firstly,the K-nearest neighbors sample set of missing data is determined according to the similarity between samples.On this basis,due to the uncertainty of missing values,the data distribution information based on the K-nearest neighbors sample set is introduced to further fill the missing data into interval form.Secondly,considering the influence of outliers in clustering,an interval fuzzy C-means algorithm for density distribution is proposed by introducing spatial density distribution of data.Finally,fuzzy C-means algorithm is carried out on the filled interval data to clustering.The experimental results show that the algorithm can effectively improve the accuracy and robustness of clustering on UCI datasets and artificial datasets.关键词
不完整数据/K近邻/模糊C均值/密度Key words
incomplete data/K-nearest neighbors/fuzzy C-means/density分类
信息技术与安全科学引用本文复制引用
张利,路颜萍,侯晴,张皓博..K近邻空间密度分布的模糊聚类算法[J].辽宁大学学报（自然科学版）,2023,50(4):289-301,13.基金项目
国家自然科学基金项目(62072220) （62072220）
辽宁省中央引导地方科技发展资金计划项目(2022JH6/100100032) （2022JH6/100100032）
辽宁省自然科学基金资助项目(2022-KF-13-06) （2022-KF-13-06）
