Fuzzy clustering algorithm for evolving data streams combined with soft constraints
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.
data stream clusteringdensity-based clusteringfuzzy clusteringconcept driftpartial discharge
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