Density Peaks Clustering Algorithm Based on Geodesic Distance and Cosine Mutual Reverse Nearest Neighbors for Manifold Datasets

ZHAO Jia; WANG Gang; LÜ Li; FAN Tang-huai

doi:10.12263/DZXB.20211273

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Density Peaks Clustering Algorithm Based on Geodesic Distance and Cosine Mutual Reverse Nearest Neighbors for Manifold Datasets

PAPERS | 更新时间：2025-12-08

- Density Peaks Clustering Algorithm Based on Geodesic Distance and Cosine Mutual Reverse Nearest Neighbors for Manifold Datasets
- ACTA ELECTRONICA SINICA Vol. 50, Issue 11, Pages: 2730-2737(2022)
- 作者机构：
  
  南昌工程学院信息工程学院，江西南昌 330099
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20211273
  CLC： TP182;TP391
- Received：17 September 2021，
  
  Revised：2022-01-01，
  
  Published：25 November 2022
- 稿件说明：
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赵嘉,王刚,吕莉等.面向流形数据的测地距离与余弦互逆近邻密度峰值聚类算法[J].电子学报,2022,50(11):2730-2737.

ZHAO Jia,WANG Gang,LÜ Li,et al.Density Peaks Clustering Algorithm Based on Geodesic Distance and Cosine Mutual Reverse Nearest Neighbors for Manifold Datasets[J].ACTA ELECTRONICA SINICA,2022,50(11):2730-2737.
赵嘉,王刚,吕莉等.面向流形数据的测地距离与余弦互逆近邻密度峰值聚类算法[J].电子学报,2022,50(11):2730-2737. DOI： 10.12263/DZXB.20211273.

ZHAO Jia,WANG Gang,LÜ Li,et al.Density Peaks Clustering Algorithm Based on Geodesic Distance and Cosine Mutual Reverse Nearest Neighbors for Manifold Datasets[J].ACTA ELECTRONICA SINICA,2022,50(11):2730-2737. DOI： 10.12263/DZXB.20211273.

摘要

密度峰值聚类算法倾向在球形分布数据中选择密度峰值，而流形数据多呈非球形分布，导致不能准确找到数据的类簇中心.该算法的分配策略优先对类簇中心附近的样本进行链式分配，而流形数据大量样本远离其类簇中心，导致本应属于同一类簇的样本被错误分配.为此，本文提出一种面向流形数据的测地距离与余弦互逆近邻密度峰值聚类算法.将

近邻与测地距离结合并重新定义局部密度，凸显密度峰值与非密度峰值的差异，准确找到类簇中心；将互逆近邻和余弦相似性相结合，得到基于余弦互逆近邻的样本相似度矩阵，为流形类簇准确分配样本.实验结果表明，本算法能有效发现流形数据集的几何形状并准确聚类，对真实数据集和图像数据集的聚类效果优秀.

Abstract

The density peaks clustering algorithm tends to select the density peaks in the spherical distribution data

while the manifold data are mostly non spherical distribution

resulting in the inability to accurately find the cluster centers. The allocation strategy of the algorithm gives priority to the chain allocation of samples near the cluster centers

while a large number of samples of manifold data are far away from the cluster centers

resulting in the wrong allocation of samples that should belong to the same cluster. Therefore

this paper proposes a density peaks clustering algorithm based on geodesic distance and cosine mutual reverse nearest neighbors for manifold datasets. Combining

-nearest neighbors with geodesic distance and redefining local density

highlighting the difference between density peaks and non density peaks

accurately find the cluster centers; combining the mutual reverse nearest neighbors and cosine similarity

the sample similarity matrix based on cosine mutual reverse nearest neighbors is obtained

which can accurately allocate samples for manifold clusters. The experimental results show that the algorithm can effectively find the geometry structure of manifold datasets

and has excellent clustering effect on real datasets and picture datasets.

关键词

Keywords

references

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