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.
The K-SCAN Clustering Algorithm
arXiv:2607. 24537v1 Announce Type: new Abstract: In the Big Data era, the scalability of clustering algorithms constitutes a key challenge.
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arXiv Machine Learning
Jul 28
Why does Greedy Search produce Optimal Clustering Outcomes? A Fixed-Core Assignment Theory
arXiv:2607. 24237v1 Announce Type: new Abstract: Many existing clustering methods are designed based on a set-oriented definition---a cluster is a set of similar points---relying a point-to-point similarity function to find similar points.
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.
arXiv Machine Learning
Jul 31
A novel k-means clustering approach using two distance measures for Gaussian data
arXiv:2511. 17823v2 Announce Type: replace Abstract: Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning.
arXiv Machine Learning
Jun 18
Seed-Guided Semi-Supervised Clustering by A-Contrario Anomaly Detection
arXiv:2606. 18833v1 Announce Type: new Abstract: This paper introduces a semi-supervised clustering framework grounded in the statistical duality between grouping principles and anomaly detection.
arXiv Machine Learning
Jul 3
Scalable and Distributed Silhouette Approximation
arXiv:2607. 01993v1 Announce Type: cross Abstract: The silhouette is one of the most widely used measures to assess the quality of a $k$-clustering of a dataset of $n$ elements.
arXiv Machine Learning
Jul 20
Data-Native Global Optimization for Big Data K-means Clustering
arXiv:2607. 15835v1 Announce Type: new Abstract: Big data clustering remains challenging: the Minimum Sum-of-Squares Clustering (MSSC) problem underlying K-means is NP-hard, and existing methods either reach poor local minima or require prohibitive metaheuristic hybrids.
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
Aug 5
Strong bounds for large-scale Minimum Sum-of-Squares Clustering
arXiv:2502. 08397v3 Announce Type: replace-cross Abstract: Clustering is a fundamental technique in data analysis and machine learning, used to group similar data points together.
arXiv Machine Learning
Jun 2
Cluster Analysis with Resampling for Validation and Exploration (CARVE)
arXiv:2606. 00327v1 Announce Type: cross Abstract: Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries.
arXiv Machine Learning
2d ago
Connected Subspace Clustering: Hardness, a Scalable Heuristic, and an Application to Sea Level Geodesy
arXiv:2608. 14215v1 Announce Type: new Abstract: Constrained optimization extends classical optimization by integrating side information, making it widely applicable across scientific and engineering domains.
arXiv Machine Learning
Jun 2
Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures
arXiv:2411. 12438v2 Announce Type: replace-cross Abstract: We develop a new approach for clustering non-spherical (i.