Spectral clustering based on the local similarity measure of shared ...

In this paper, we propose a novel spectral clustering algorithm based on the local similarity measure of shared neighbors.

Numerical experiments demonstrate that the proposed algorithm outperforms other existing spectral clustering algorithms in terms of the clustering ...

Spectral clustering based on the local similarity measure of shared ...

The critical step of spectral clustering is the similarity measurement, which largely determines the performance of the spectral clustering method. In this ...

Spectral clustering based on the local similarity measure of shared ...

Discover this 2022 paper in ETRI Journal by Cao, Zongqi; Chen, Hongjia; and, Wang, Xiang.

Spectral clustering based on the local similarity measure of shared ...

Spectral clustering based on the local similarity measure of shared neighbors. Language: English; Authors: Cao, Zongqi1 (AUTHOR) Chen, Hongjia1 (AUTHOR) Wang ...

Spectral clustering based on the local similarity measure of shared ...

In this paper, we propose a novel spectral clustering algorithm based on the local similarity measure of shared neighbors. This similarity measurement ...

Spectral clustering - Wikipedia

In multivariate statistics, spectral clustering techniques make use of the spectrum (eigenvalues) of the similarity matrix of the data to perform ...

[PDF] Clustering Using a Similarity Measure Based on Shared Near ...

A nonparametric clustering technique incorporating the concept of similarity based on the sharing of near neighbors is presented, which is an essentially ...

Local density adaptive similarity measurement for spectral clustering

Similarity measurement is crucial to the performance of spectral clustering. The Gaussian kernel function is usually adopted as the similarity measure.

An Adaptive Spectral Clustering Algorithm Based on the Importance ...

The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is ...

Improved Spectral Clustering Algorithm Based on Similarity Measure

Aimed at the Gaussian kernel parameter σ sensitive issue of the traditional spectral clustering algorithm, this paper proposed to utilize the similarity ...

Construction of the similarity matrix for the spectral clustering method

Spectral clustering is a powerful method for finding structure in a dataset through the eigenvectors of a similarity matrix.

Modelling and application of a spectral clustering method for shared ...

Cluster analysis methods are mainly divided into hierarchical, density-based, grid and model-based clustering. Hierarchical clustering methods include k-means ( ...

A local mean-based distance measure for spectral clustering

Constructing a similarity graph based on an appropriate distance measure for modeling the local neighborhood relations among data samples is crucial for ...

A novel spectral clustering algorithm based on neighbor relation and ...

(2022). Spectral clus- tering based on the local similarity measure of shared neighbors. ETRI Journal. Chen, Y., Tang, ...

In this paper, a multi-similarity spectral (MSSC) method is proposed as an improvement to the former evolutionary clustering method.

Spectral Clustering Algorithm Based on Local Sparse Representation

For a given sample the algorithm solves l 1-minimization with the local nearest neighborhood as dictionary, constructs the similarity matrix by calculating ...

An Adaptive Spectral Clustering Algorithm Based on the Importance ...

The Gaussian kernel function is one of the most common similarity measures for spectral clustering, in which a scaling parameter σ controls the speed of the ...

The commonly used similarity measure in the clustering algorithms is the Gaussian kernel function which uses sensitive scaling parameter and when applied to the ...

An Improvement of Spectral Clustering via Message Passing and ...

algorithm based on manifold similarity measure,'' in ... Yu, ''Local density adaptive similarity measurement for spectral clustering,'' Pattern ...