Research map / Learning theory and algorithms / Clustering
Scalable and Globally Optimal Generalized L₁ K-center Clustering via Constraint Generation in Mixed Integer Linear Programming
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use globally optimized kc-Opt instead of the strongest listed baseline | L1 k-center clustering · 20 real benchmark datasets | L1 k-center objective ε | Helped |
| Add constraint generation to solve the MILP iteratively, adding violated farthest-point constraints | Generalized L1 k-center MILP · Norm synthetic datasets | number of constraints generated: ≈450 | Helped |
| Add outlier exclusion to the objective, removing l observations (ablation) | kc-Opt · Norm-Out synthetic dataset | L1 k-center objective ε | Helped |
| Use kc-OptOut instead of the outlier-aware k,z-center baseline | Outlier-aware L1 k-center clustering · Norm-Out synthetic dataset | L1 k-center objective ε | Helped |
Closest papers by meaning
- Efficient Constrained K-center Clustering with Background Knowledge (AAAI 2024 · similarity 0.93)
- Improved Guarantees for Fully Dynamic $k$-Center Clustering with Outliers in General Metric Spaces (NeurIPS 2024 · similarity 0.90)
- Competitively Consistent Clustering (ICML 2025 · similarity 0.88)
- Fully-Scalable Massively Parallel Algorithm for k-center with Outliers (AAAI 2025 · similarity 0.88)
- Average Sensitivity of Hierarchical $k$-Median Clustering (ICML 2025 · similarity 0.88)
- Ultrametric Cluster Hierarchies: I Want ‘em All! (NeurIPS 2025 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.