Weakly supervised anomaly detection is a challenging task since frame-level labels are not
given in the training phase. Previous studies generally employ neural networks to learn …

In recent years, a significant boost in data availability for persistent data streams has been
observed. These data streams are continually evolving, with the clusters frequently forming …

Density Peaks Clustering (DPC) is a density-based clustering algorithm that has the
advantage of not requiring clustering parameters and detecting non-spherical clusters. The …

Since most existing single-prototype clustering algorithms are unsuitable for complex-
shaped clusters, many multi-prototype clustering algorithms have been proposed …

Density peaks clustering (DPC) algorithm is a novel density-based clustering algorithm,
which is simple and efficient, is not necessary to specify the number of clusters in advance …

The density peaks clustering (DPC) algorithm is a classical and widely used clustering
method. However, the DPC algorithm requires manual selection of cluster centers, a single …

Abstract Although Density Peak Clustering (DPC) can easily locate cluster centers by
detecting density peaks in its decision graph, its allocation strategy may unadvisedly …

Aiming at the problems that the initial cluster centers are randomly selected and the number
of clusters is manually determined in traditional clustering algorithm, which results in …

The clustering results of the density peak clustering algorithm (DPC) are greatly affected by
the parameter dc, and the clustering center needs to be selected manually. To solve these …

The detection of partial discharge (PD) is a crucial method to evaluate the insulation status
of transformers. The main difficulties of the current localization algorithms are the complexity …