Density peak clustering algorithms: A review on the decade 2014–2023
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 …
during the last decade, The research communities believe that DPC is a powerful tool …
A method of two-stage clustering learning based on improved DBSCAN and density peak algorithm
Density peak (DP) and density-based spatial clustering of applications with noise (DBSCAN)
are the representative clustering algorithms on the basis of density in unsupervised learning …
are the representative clustering algorithms on the basis of density in unsupervised learning …
VDPC: Variational density peak clustering algorithm
The widely applied density peak clustering (DPC) algorithm makes an intuitive cluster
formation assumption that cluster centers are often surrounded by data points with lower …
formation assumption that cluster centers are often surrounded by data points with lower …
Energy-aware scheduling in edge computing with a clustering method
Y Hao, J Cao, Q Wang, J Du - Future Generation Computer Systems, 2021 - Elsevier
With the development of Cloud and 5G technology, edge devices have been widely used in
various areas. However, the limited battery energy and processing ability of edge devices …
various areas. However, the limited battery energy and processing ability of edge devices …
Anchor-based incomplete multi-view spectral clustering
In the past decade, multi-view clustering has become a research hot spot of machine
learning. In traditional multi-view clustering methods, all views of the data points are …
learning. In traditional multi-view clustering methods, all views of the data points are …
Adaptive weighted dictionary representation using anchor graph for subspace clustering
Samples are commonly represented as sparse vectors in many dictionary representation
algorithms. However, this method may result in loss of discriminatory information. Moreover …
algorithms. However, this method may result in loss of discriminatory information. Moreover …
One-Shot Federated Clustering Based on Stable Distance Relationships
Federated clustering (FC) is an emerging and important topic in data clustering research.
However, for existing works, there are two challenging issues as follows. 1) FC does not …
However, for existing works, there are two challenging issues as follows. 1) FC does not …
A novel density deviation multi-peaks automatic clustering algorithm
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 …
method. However, the DPC algorithm requires manual selection of cluster centers, a single …
Fault prognostics for photovoltaic inverter based on fast clustering algorithm and Gaussian mixture model
The fault prognostics of the photovoltaic (PV) power generation system is expected to be a
significant challenge as more and more PV systems with increasingly large capacities …
significant challenge as more and more PV systems with increasingly large capacities …
ACQC-LJP: Apollonius circle-based quantum clustering using Lennard-Jones potential
Quantum Clustering (QC) is widely regarded as a powerful method in unsupervised learning
problems. This method forms a potential function using a wave function as a superposition of …
problems. This method forms a potential function using a wave function as a superposition of …