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Graph analysis using a GPU-based parallel algorithm: quantum clustering
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DOI：10.1007/s10489-024-05587-8.png)
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Abstract
En 中文
The article introduces a new method for applying Quantum Clustering to graph structures. Quantum Clustering (QC) is a density-based unsupervised learning method that determines cluster centers by constructing a potential function. In this method, we develop the Graph Gradient Descent algorithm to find the centers of clusters for graph analysis. GPU parallelization is utilized for computing potential values. We also conducted comparative experiments on five widely used datasets and evaluated them using four indicators. The results show superior performance of our method. Finally, we discuss the influence of the crucial parameter sigma\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} on the experimental results.
Keywords:
Quantum clustering
Graph clustering
Graph gradient descent
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