arXiv Machine Learning
Jul 10
Persistent Multiscale Density-based Clustering
arXiv:2512. 16558v3 Announce Type: replace Abstract: Clustering is a cornerstone of modern data analysis.
Automatic depth-based local center clustering via $\beta$-integrated local depth and adaptive grouping
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arXiv Machine Learning
Sep 17
Bracketing Uncertainty in Clustering Under the Manifold Hypothesis
The paper formalizes a geometric tradeoff between ambient separation and sampling gaps to determine when distinct manifold components can be reliably separated in clustering. It introduces a threshold phenomenon for mutual‑k‑nearest‑neighbor graphs, defining an uncertainty zone where the number of clusters cannot be identified. The authors propose Manifold‑Based Clustering (MBC), which outputs a bracket interval quantifying this uncertainty rather than forcing a single cluster count.
arXiv AI
Aug 10
Density-aware Hierarchical Clustering Based on Element-Categorized Connection Subgraphs
arXiv:2608. 06990v1 Announce Type: cross Abstract: Clustering is a fundamental data mining technique for pattern recognition through unsupervised learning.
Hugging Face Trending Papers
Aug 5
HiSC: Hierarchical Spatial Clustering Token Compression for Efficient 3D Scene Understanding
3D vision-language models (3D VLMs) enable spatial reasoning over multi-view scenes but suffer from substantial token redundancy due to duplicated observations and large uninformative regions, leading to high computational cost. Although visual token compression has shown promise in accelerating 2D VLMs, it fails to capture the structured nature of 3D scenes and leads to incomplete spatial coverage and loss of fine-grained details.
arXiv Machine Learning
Jul 22
Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration
arXiv:2505. 04346v2 Announce Type: replace Abstract: Clustering aims at partitioning data points into groups of similar objects without knowing about the class labels.
arXiv Machine Learning
Sep 23
Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approach
arXiv:2609.26063v1 Announce Type: new Abstract: Federated graph learning (FGL) enables multiple clients to collaboratively train graph models without sharing their private graph data, providing a pro...
arXiv Statistics ML
2d ago
Gradient-Guided Density Peak Clustering
Gradient-Guided Density Peak Clustering (GGDPC) enhances traditional density peak clustering by performing a gradient ascent step before each nearest‑neighbor uphill search, aiming to stabilize uphill paths in low‑density regions. The authors develop a stability theory linking the GGDPC graph to the gradient ascent flow of the population density, and establish consistency across five criteria: recovery of local modes, adjusted Rand index, dendrogram (cluster tree), path length, and waterfall measure. These results offer new statistical, geometric, and topological insights into DPC‑type clustering algorithms.
arXiv Computer Vision
Sep 16
Geometry vs Structure: Graph-Based Diagnostics for LiDAR Point-Cloud Simulation Fidelity
arXiv:2609.16378v1 Announce Type: cross Abstract: Digital twins provide a scalable and cost-effective complement to real-world testing for validating autonomous-driving and advanced driver-assistance...
arXiv AI
Sep 10
MSSP: Multi-Scale Spatially-Constrained Partition for Unsupervised Semantic Segmentation of 3D Point Clouds
arXiv:2609.06959v1 Announce Type: cross Abstract: 3D point cloud semantic segmentation is essential for real-world spatial understanding, yet the prohibitive cost of human annotations motivates unsup...
arXiv Machine Learning
Jun 5
Central Description Length (CDL) Clustering Validation Index
arXiv:2606. 05230v1 Announce Type: cross Abstract: Selecting a clustering algorithm and its hyperparameters without labels is a common difficulty in engineering machine learning pipelines that work with unsupervised analysis of sensor, image, or process data.
arXiv Machine Learning
Aug 18
A Deep Learning Model for Spatially Clustered Data via Differentiable Cluster Assignment
arXiv:2608. 14968v1 Announce Type: cross Abstract: We consider nonparametric regression when the association between a response and its covariates changes across an unknown partition of a spatial domain.
arXiv Machine Learning
Jun 2
Robust Contrastive Graph Clustering with Adaptive Local-Global Integration
arXiv:2605. 28209v2 Announce Type: replace Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities.