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 …

Internal cluster validity measures (such as the Calinski–Harabasz, Dunn, or Davies–Bouldin
indices) are frequently used for selecting the appropriate number of partitions a dataset …

Abstract Density Peaks Clustering (DPC) is a classic density-based clustering algorithm that
has been successfully applied in various areas. However, it assigns samples based on their …

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 …

Border-Peeling algorithm is a recently proposed density based clustering algorithm. The
method of peeling off border points by continuous iteration and calculating the density …

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 …

Density peak clustering (DPC) has garnered growing interest over recent decades due to its
capability to identify clusters with diverse shapes and its resilience to the presence of noisy …

Cutset-type possibilistic c-means (CPCM) is an improved version of possibilistic c-means
(PCM), and it addresses the issue of consistency clustering in PCM. However, the clustering …

Density peaks clustering (DPC) is a popular clustering algorithm, which has been studied
and favored by many scholars because of its simplicity, fewer parameters, and no iteration …