Clustering, a traditional machine learning method, plays a significant role in data analysis.
Most clustering algorithms depend on a predetermined exact number of clusters, whereas …

Given a large unlabeled set of complex data, how to efficiently and effectively group them
into clusters remains a challenging problem. Density peaks clustering (DPC) algorithm is an …

The aggregation of individuals facilitates local information exchange, and the migration of
individuals from one population to another leads to a dynamic community structure. In …

Data mining is a technique that allows to obtain patterns or models from the gathered data.
This technique is applied in all kind of environments such as in the biological field …

Density peaks clustering (DPC) algorithm is proposed to identify the cluster centers quickly
by drawing a decision-graph without any prior knowledge. Meanwhile, DPC obtains arbitrary …

The massive growth of data in the network leads to attacks or intrusions. An intrusion
detection system detects intrusions from high volume datasets but increases complexities. A …

This paper focuses on the real-time dynamic clustering analysis of power load data based
on the dynamic conditional score (DCS) model, and an autocorrelation increment fuzzy C …

Emotional dysregulation is a construct that has drawn substantial attention as a
transdiagnostic contributing factor to the loss of health. Intimate partner violence (IPV) is a …

A challenging issue of clustering in real-word application is to detect clusters with arbitrary
shapes and densities in complex data. Many conventional clustering algorithms are capable …

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of
the smart grid and smart meter, such as demand response, asset management, investment …