Density peak clustering (DPC) algorithm has become a well-known clustering method
during the last decade, The research communities believe that DPC is a powerful tool …

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 …

Learning from imbalanced datasets poses a major challenge in data mining community.
When dealing with imbalanced datasets, conventional classification algorithms generally …

Anti-terrorism research is an important academic topic in current societies. The crucial
features of attacked incidents can be obtained effectively by identifying phase division of …

Density peak clustering (DPC) is a recently developed density-based clustering algorithm
that achieves competitive performance in a non-iterative manner. DPC is capable of …

Density-based clustering is one of the most commonly used analysis methods in data mining
and machine learning, with the advantage of locating non-ball-shaped clusters without …

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) model focuses on searching density peaks and clustering
data with arbitrary shapes for machine learning. However, it is difficult for DPC to select a cut …

Imbalanced datasets classification remains an important domain in machine learning.
Conventional supervised learning algorithms tend to be biased towards the majority class …

Density peaks clustering (DPC) is a promising algorithm due to straightforward and easy
implementation. However, most of its improvements still rely on expert, strong prior …