GraphLSHC: towards large scale spectral hypergraph clustering

DOI10.1016/j.ins.2020.07.018zbMath1475.68306OpenAlexW3045467356MaRDI QIDQ2054077

Leong Hou U, Zhifeng Hao, Sucheng Deng, Yiyang Yang, Zhiguo Gong, Yuhong Li, Juan Lu

Publication date: 30 November 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.07.018

zbMATH Keywords

hypergraphclusteringunsupervised learningmachine learning

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Hypergraphs (05C65)

Related Items (5)

GraphLSH ⋮ FGC\_SS: fast graph clustering method by joint spectral embedding and improved spectral rotation ⋮ Large-scale non-negative subspace clustering based on Nyström approximation ⋮ UP-DPC: ultra-scalable parallel density peak clustering ⋮ Identifying lead users in online user innovation communities based on supernetwork

Uses Software

• RCV1
• DeepWalk
• LINE

Cites Work

• Unnamed Item
• Spectral based hypothesis testing for community detection in complex networks
• Approximate normalized cuts without eigen-decomposition
• Fast computation of low-rank matrix approximations
• Local Deep-Feature Alignment for Unsupervised Dimension Reduction
• Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification
• Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix

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