Data clustering is a commonly used data processing technique in many fields, which divides
objects into different clusters in terms of some similarity measure between data points …

As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …

Abstract The well-known Density Peak Clustering algorithm (DPC) proposed a heuristic
center detection idea, ie, to find density peaks as cluster centers. Nevertheless, such a …

Three-way clustering has been an active research topic in the field of cluster analysis in
recent years. Some efforts are focused on the technique due to its feasibility and rationality …

In this paper, a novel non-parametric clustering algorithm which is based on the concept of
divide-and-merge is proposed. The proposed algorithm is based on two primary phases …

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 …

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

Clustering can be divided into five categories: partitioning, hierarchical, model-based,
density-based, and grid-based algorithms. Among them, grid-based clustering is highly …

A growing number of applications generate streaming data, making data stream mining a
popular research topic. Classification-based streaming algorithms require pre-training on …

Clustering plays an essential role in machine learning and data mining. Many real-world
datasets for clustering are often different but related in the big data era. Recent research …