Unsupervised space-time clustering using persistent homology

DOI10.1002/env.2539zbMATH Open1545.62808MaRDI QIDQ6626070

Umar Islambekov, Yulia R. Gel

Publication date: 28 October 2024

Published in: Environmetrics (Search for Journal in Brave)

zbMATH Keywords

unsupervised learningclustering algorithmBetti numberstopological data analysis

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Cites Work

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• Confidence sets for persistence diagrams
• Rates of convergence for robust geometric inference
• Morse theory for filtrations and efficient computation of persistent homology
• Summarizing numeric spatial data streams by trend cluster discovery
• Computing persistent homology
• Topological persistence and simplification
• Hierarchical clustering schemes
• The Structure and Stability of Persistence Modules
• Topology and data
• A Graph-Theoretic Approach to Nonparametric Cluster Analysis
• Stability of persistence diagrams
• Proximity of persistence modules and their diagrams
• Persistence-Based Clustering in Riemannian Manifolds
• Statistical topological data analysis using persistence landscapes
• Barcodes: The persistent topology of data
• Riding down the Bay: space-time clustering of ecological trends

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