Research map / Learning theory and algorithms / Clustering
Incremental Density-Based Clustering with Grid Partitioning (Student Abstract)
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use grid partitioning to localize update regions and restrict ε-range queries | Incremental DBSCAN (iDBSCAN) · circles (n = 10k) | RT (sec): 66.10 -> 0.84 | Helped |
| Use grid partitioning to localize update regions and restrict ε-range queries | Incremental DBSCAN (iDBSCAN) · circles (n = 30k) | RT (sec): 924.00 -> 7.76 | Helped |
| Use grid partitioning to localize update regions and restrict ε-range queries | Incremental DBSCAN (iDBSCAN) · blobs (d = 3) | RT (sec): 38.03 -> 8.30 | Helped |
| Use grid partitioning to localize update regions and restrict ε-range queries | Incremental DBSCAN (iDBSCAN) · blobs (d = 5) | RT (sec): 43.26 -> 14.44 | Helped |
| Use grid partitioning to localize update regions and restrict ε-range queries | Incremental DBSCAN (iDBSCAN) · moons (η = 0.1) | RT (sec): 12.54 -> 0.19 | Helped |
| Use grid partitioning to localize update regions and restrict ε-range queries | Incremental DBSCAN (iDBSCAN) · moons (η = 0.2) | RT (sec): 12.25 -> 0.15 | Helped |
| Use grid partitioning to localize update regions and restrict ε-range queries | Incremental DBSCAN (iDBSCAN) · synthetic datasets in Table 1 | ACC (%): 99.9, 99.9, 97.7, 97.6, 99.2, 98.7 | Mixed |
Closest papers by meaning
- DBSCAN++: Towards fast and scalable density clustering (ICML 2019 · similarity 0.88)
- Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering (AAAI 2019 · similarity 0.85)
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- Scalable DBSCAN with Random Projections (NeurIPS 2024 · similarity 0.83)
- Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability (ICML 2025 · similarity 0.82)
- Ultrametric Cluster Hierarchies: I Want ‘em All! (NeurIPS 2025 · similarity 0.82)
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