Density peaks clustering (DPC) algorithm is a new algorithm based on density clustering
analysis, which can quickly obtain the cluster centers by drawing the decision diagram by …

DPC (Clustering by fast search and find of Density Peaks) algorithm and its variations
typically employ Euclidean distance, overlooking the diverse contributions of individual …

Fast and efficient are common requirements for all clustering algorithms. Density peaks
clustering algorithm (DPC) can deal with non-spherical clusters well. However, due to the …

Abstract Density Peaks Clustering (DPC) algorithm is a kind of density-based clustering
approach, which can quickly search and find density peaks. However, DPC has deficiency in …

Intrusion detection systems are crucial in fighting against various network attacks. By
monitoring the network behavior in real time, possible attack attempts can be detected and …

The density peak clustering (DPC) algorithm is an efficient clustering algorithm that can
automatically find the class center and realize arbitrary shape data clustering. The design of …

Background The expectation maximization (EM) algorithm is a common tool for estimating
the parameters of Gaussian mixture models (GMM). However, it is highly sensitive to initial …

In density peaks clustering, its complexity for computing local density and relative distance of
samples raises a scalability issue for processing large datasets. To address the issue …

The k-modes clustering algorithm was proposed by Huang for handling datasets with
categorical attributes, however, the dissimilarity measure used limits its applicability. Ng et …

Density peaks clustering is a novel and efficient density-based clustering algorithm.
However, the problem of the sensitive information leakage and the associated security risk …