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September 10, 2025IEEE Transactions on Pattern Analysis and Machine Intelligence

Robust Density Peaks Clustering for Manifold Data with Multiple Peaks

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Authors

LDLing DingCLChao LiSDShifei Ding

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Overview

Robust density peaks clustering reduces sensitivity in determining clusters in manifold data, indicating enhanced accuracy.

Key Points

  • Robust density peaks clustering outperforms traditional methods by accommodating manifold structures effectively.
  • Local density calculations are optimized through mutual K-nearest neighbors, enhancing clustering robustness.
  • The Davies-Bouldin Index based on Minimum Spanning Tree helps adaptively select the ideal number of classes.
  • Numerous experiments confirm RDPCM's superior effectiveness compared to advanced clustering algorithms.

Cite This Study

Ding et al. (2025) studied this question.

synapsesocial.com/papers/68c1a40f54b1d3bfb60dec0fhttps://doi.org/10.1109/tpami.2025.3594121
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Also Consider

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  1. 1An Improved Density Peak Clustering with Flexible Manifold Distance and Natural Nearest Neighbors for Network Intrusion Detection2024
  2. 2Density Peaks Clustering Based on Weighted Density Estimating and Multicluster Merging2025 · 1 citations
  3. 3DPC-DIST: an improved density peak clustering algorithm based on geometric distribution2026
  4. 4An Improved Density Peaks Clustering Algorithm Based On Density Ratio2024 · 2 citations
  5. 5An improved density peaks clustering algorithm based on mutual nearest neighbor distance2024