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BlockDPC: Distributed density peak clustering for large-scale data
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DOI：10.1016/j.patcog.2026.113357.png)
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Abstract
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• The proposed BlockDPC balances time complexity and clustering accuracy. • We offer a solid theory for sampling in large-scale data clustering. • Compared with 14 methods, BlockDPC achieves the best performances in most cases.
Keywords:
BlockDPC
density peak clustering
large-scale data
distributed clustering
sampling theory
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
Fast main density peak clustering within relevant regions via a robust decision graph.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveShareSaveShareSaveDensity peaks clustering based on density backbone and fuzzy neighborhoodPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveA Sampling-Based Density Peaks Clustering Algorithm for Large-Scale DataPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveIdentifying cluster centroids from decision graph automatically using a statistical outlier detection methodNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSaveShareSaveShareSave
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