Research map / Learning theory and algorithms / Clustering
XClusters: Explainability-First Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly optimize trend similarity and demographic-feature similarity with evolutionary Pareto-based clustering | XClusters · Credit card dataset and DS4C | total within-cluster variance and weighted average F1 | Helped |
| Cluster by time-series trends only instead of balancing trend and feature similarity | XClusters · Credit card dataset and DS4C | weighted average F1 | Hurt |
| Cluster by demographic features only instead of balancing trend and feature similarity | XClusters · Credit card dataset and DS4C | total within-cluster variance | Hurt |
| Use Feature→Time series lexicographic clustering | XClusters · Credit card dataset and DS4C | weighted average F1 and total within-cluster variance | Mixed |
| Use overlapping Pareto clusters instead of evolutionary clustering | XClusters · Credit card dataset and DS4C | explainability | Hurt |
Closest papers by meaning
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- Explaining Kernel Clustering via Decision Trees (ICLR 2024 · similarity 0.86)
- Ultrametric Cluster Hierarchies: I Want ‘em All! (NeurIPS 2025 · similarity 0.83)
- Learning-Augmented Hierarchical Clustering (ICML 2025 · similarity 0.83)
- CohEx: A Generalized Framework for Cohort Explanation (AAAI 2025 · similarity 0.83)
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