The mergegram of a dendrogram and its stability
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Abstract (arXiv)
This paper extends the key concept of persistence within Topological Data Analysis (TDA) in a new direction. TDA quantifies topological shapes hidden in unorganized data such as clouds of unordered points. In the 0-dimensional case the distance-based persistence is determined by a single-linkage (SL) clustering of a finite set in a metric space. Equivalently, the 0D persistence captures only edge-lengths of a Minimum Spanning Tree (MST). Both SL dendrogram and MST are unstable under perturbations of points. We define the new stable-under-noise mergegram, which outperforms previous isometry invariants on a classification of point clouds by PersLay.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jul 2020.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.
3 papers.
- 2023Recognizing Rigid Patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants with no False Negatives and no False Positivescited by 7
- 2024Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Predictioncited by 2
- 2026Quotient Geometry and Persistence-Stable Metrics for Swarm Configurationscited by 0
- 2026Quotient Geometry and Persistence-Stable Metrics for Swarm Configurations14 Mar 2026
- 2024Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction7 Feb 2024
- 2023Recognizing Rigid Patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants with no False Negatives and no False Positives27 Mar 2023