Spectral clustering based on the local similarity measure of shared neighbors
Spectral clustering based on the local similarity measure of shared neighbors
复制标题
DOI：
10.4218/etrij.2021-0230
复制
发表时间：
2022-05
期刊：
影响因子：
1.4
通讯作者：
Zongqi Cao;Hongjia Chen;Xiang Wang
中科院分区：
文献类型：
--
作者：
Zongqi Cao;Hongjia Chen;Xiang Wang
谱聚类已成为一种典型且高效的聚类方法，在各种应用中得到了广泛的应用。谱聚类的关键步骤是相似性度量，它在很大程度上决定了谱聚类方法的性能。本文提出了一种新的基于共享邻居局部相似性度量的谱聚类算法。这种相似性度量利用了基于共享邻居权重的数据点之间的局部密度信息，该有向k-近邻图只有一个参数k，即最近邻居的数量。在人工数据集和真实数据集上的数值实验表明，我们提出的算法在归一化互信息、聚类精度和F度量方面的聚类性能优于其他现有的谱聚类算法。以大豆数据集为例，该方法的聚类性能提高了15.82%。
Spectral clustering has become a typical and efficient clustering method used in a variety of applications. The critical step of spectral clustering is the similarity measurement, which largely determines the performance of the spectral clustering method. In this paper, we propose a novel spectral clustering algorithm based on the local similarity measure of shared neighbors. This similarity measurement exploits the local density information between data points based on the weight of the shared neighbors in a directed k ‐nearest neighbor graph with only one parameter k , that is, the number of nearest neighbors. Numerical experiments on synthetic and real‐world datasets demonstrate that our proposed algorithm outperforms other existing spectral clustering algorithms in terms of the clustering performance measured via the normalized mutual information, clustering accuracy, and F ‐measure. As an example, the proposed method can provide an improvement of 15.82% in the clustering performance for the Soybean dataset.