On the public citation graph

Density Peaks Clustering Based on Candidate Center and Multi Assignment Policies

https://doi.org/10.1109/access.2023.3283561
0 signed · 39 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/stc.3138openalex-snapshot
Automatic identification of structural modal parameters based on density peaks clustering algorithm
inferredcites →https://doi.org/10.1002/widm.1219openalex-snapshot
Algorithms for hierarchical clustering: an overview, II
inferredcites →https://doi.org/10.1002/widm.30openalex-snapshot
Density‐based clustering
inferredcites →https://doi.org/10.1002/widm.53openalex-snapshot
Algorithms for hierarchical clustering: an overview
inferredcites →https://doi.org/10.1007/s00500-020-05028-xopenalex-snapshot
A two-stage density clustering algorithm
inferredcites →https://doi.org/10.1007/s13042-017-0636-1openalex-snapshot
A robust density peaks clustering algorithm using fuzzy neighborhood
inferredcites →https://doi.org/10.1016/j.eswa.2018.07.075openalex-snapshot
Dynamic graph-based label propagation for density peaks clustering
inferredcites →https://doi.org/10.1016/j.inffus.2006.05.006openalex-snapshot
k-ANMI: A mutual information based clustering algorithm for categorical data
inferredcites →https://doi.org/10.1016/j.ins.2016.03.011openalex-snapshot
Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors
inferredcites →https://doi.org/10.1016/j.ins.2018.03.031openalex-snapshot
Shared-nearest-neighbor-based clustering by fast search and find of density peaks
inferredcites →https://doi.org/10.1016/j.ins.2022.11.091openalex-snapshot
VDPC: Variational density peak clustering algorithm
inferredcites →https://doi.org/10.1016/j.knosys.2022.109374openalex-snapshot
An improved density peak clustering algorithm guided by pseudo labels
inferredcites →https://doi.org/10.1016/j.patcog.2004.07.012openalex-snapshot
Scalable model-based cluster analysis using clustering features
inferredcites →https://doi.org/10.1016/j.patcog.2010.04.021openalex-snapshot
SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index
inferredcites →https://doi.org/10.1016/j.patcog.2020.107449openalex-snapshot
Density peaks clustering based on density backbone and fuzzy neighborhood
inferredcites →https://doi.org/10.1016/j.patcog.2020.107452openalex-snapshot
Adaptive core fusion-based density peak clustering for complex data with arbitrary shapes and densities
inferredcites →https://doi.org/10.1016/j.patrec.2009.09.011openalex-snapshot
Data clustering: 50 years beyond K-means
inferredcites →https://doi.org/10.1080/01621459.1983.10478008openalex-snapshot
A Method for Comparing Two Hierarchical Clusterings
inferredcites →https://doi.org/10.1109/access.2019.2904254openalex-snapshot
Density Peaks Clustering Based on Weighted Local Density Sequence and Nearest Neighbor Assignment
inferredcites →https://doi.org/10.1109/access.2020.2989460openalex-snapshot
Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic Distances
inferredcites →https://doi.org/10.1109/access.2020.3003057openalex-snapshot
Adaptive Density Peaks Clustering Based on K-Nearest Neighbor and Gini Coefficient
inferredcites →https://doi.org/10.1109/tkde.2014.2310215openalex-snapshot
Incremental Affinity Propagation Clustering Based on Message Passing
inferredcites →https://doi.org/10.1109/tpami.2005.69openalex-snapshot
An EM algorithm for the block mixture model
inferredcites →https://doi.org/10.1126/science.1136800openalex-snapshot
Clustering by Passing Messages Between Data Points
inferredcites →https://doi.org/10.1126/science.1242072openalex-snapshot
Clustering by fast search and find of density peaks
inferredcites →https://doi.org/10.1145/3068335openalex-snapshot
DBSCAN Revisited, Revisited
inferredcites →https://doi.org/10.1504/ijbic.2020.105899openalex-snapshot
Improved density peaks clustering based on firefly algorithm
inferredcites →https://doi.org/10.2196/29749openalex-snapshot
The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review
inferredcites →https://doi.org/10.3390/coatings12101440openalex-snapshot
Assessment of Defects under Insulation Using K-Medoids Clustering Algorithm-Based Microwave Nondestructive Testing
inferredcites →https://doi.org/10.5555/1756006.1953024openalex-snapshot
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
inferredcites →https://doi.org/10.7544/issn1000-1239.2020.20190104openalex-snapshot
Survey on Density Peak Clustering Algorithm

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4749750openalex-extracted
Density Clustering Algorithm Based on Nearest Neighbor Relationship and Relevant Space
inferred← citeshttps://doi.org/10.1007/s10586-024-04592-3openalex-snapshot
An improved density peaks clustering algorithm using similarity assignment strategy with K-nearest neighbors
inferred← citeshttps://doi.org/10.1109/access.2025.3563990openalex-snapshot
Density Peaks Clustering Algorithm Based on Neighborhood Radius and Membership Degree
inferred← citeshttps://doi.org/10.1177/14727978251364456openalex-snapshot
News text clustering optimization based on improved DPC algorithm
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4076055/v1openalex-snapshot
An improved density peaks clustering algorithm use similarity assignment strategy with K-nearest neighbors
inferred← citeshttps://doi.org/10.2139/ssrn.4822450openalex-snapshot
Density Clustering Algorithm Based on Effective Neighbors and Multi-Stage Allocation Strategy
inferred← citeshttps://doi.org/10.2139/ssrn.5202377openalex-snapshot
Density Clustering Algorithm Based On Effective Neighbors and Multi-Stage Allocation Strategy
inferred← citeshttps://doi.org/10.32604/cmc.2024.046314openalex-snapshot
Density Clustering Algorithm Based on KD-Tree and Voting Rules
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.