重庆邮电大学学报（自然科学版）2024，Vol.36Issue(2)：287-298,12.DOI:10.3979/j.issn.1673-825X.202301090007
结合软约束的演化数据流模糊聚类算法
Fuzzy clustering algorithm for evolving data streams combined with soft constraints
摘要
Abstract
In multi-source partial discharge(PD)detection,distinct PD signals exist simultaneously and change constant-ly,which makes signal separation more challenging.This situation also exists in many scenarios of clustering analysis of da-ta streams.To adapt to the heterogeneous density within the cluster and overlapping borders between clusters,and to track the drift and evolution of data streams in time,this paper proposes a real-time data stream fuzzy clustering algorithm com-bined with soft constraints.Firstly,two fuzzy soft constraints are introduced to describe the uncertainty in the distance and density of micro-clusters(mc).These micro-clusters are divided into core-mc,border-mc and outlier-mc based on thresh-olds.Secondly,fuzzy membership degrees are used at the edge of the clusters to estimate the possibility of mc belonging to different types of clusters,ensuring the integrity and improving the clustering effect.Finally,the method uses a two-stage procedure and time window models to endow the algorithm with adaptability to changing data streams and lower memory oc-cupancy.Experiments on various dataset show that the clustering effect of this algorithm is improved by 1%～3%and the average runtime is shortened by 5%～20%compared with counterparts.Separation performance is also verified in the hard-ware platform test.关键词
数据流聚类/密度聚类/模糊聚类/概念漂移/局部放电Key words
data stream clustering/density-based clustering/fuzzy clustering/concept drift/partial discharge分类
信息技术与安全科学引用本文复制引用
代少升,边志奇,袁中明..结合软约束的演化数据流模糊聚类算法[J].重庆邮电大学学报（自然科学版）,2024,36(2):287-298,12.基金项目
校企合作项目(SET2019062702) The School-Enterprise Cooperation Project(SET2019062702) （SET2019062702）
