Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Ensemble K-means× | Semi-övervakad HDBSCAN× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2002 | 2017–present |
| Upphovsperson≠ | Strehl, A. & Ghosh, J. | McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors |
| Typ≠ | Ensemble clustering (consensus aggregation of K-means partitions) | Semi-supervised density-based clustering |
| Ursprungskälla≠ | Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617. link ↗ | McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI ↗ |
| Alias | consensus K-means, K-means ensemble clustering, cluster ensemble with K-means, EKM | Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN |
| Närliggande≠ | 3 | 6 |
| Sammanfattning≠ | Ensemble K-means runs K-means clustering many times under varied initializations, random seeds, or feature subsets, then aggregates the resulting partitions into a single consensus assignment. This approach reduces K-means' well-known sensitivity to initialization and produces more stable, reproducible clusters than any single run. | Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge. |
| ScholarGateDatamängd ↗ |
|
|