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| OPTICS× | DBSCAN× | HDBSCAN× | |
|---|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning | Machine learning |
| Godina nastanka≠ | 1999 | 1996 | 2013 |
| Tvorac≠ | Ankerst, M.; Breunig, M. M.; Kriegel, H.-P.; Sander, J. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Campello, R. J. G. B.; Moulavi, D.; Sander, J. |
| Tip≠ | Density-based clustering (reachability ordering) | Density-based clustering algorithm | Hierarchical density-based clustering |
| Temeljni izvor≠ | Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD Record, 28(2), 49–60. DOI ↗ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231. link ↗ | Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg. DOI ↗ |
| Drugi nazivi≠ | OPTICS, Ordering Points To Identify the Clustering Structure, density-based clustering with reachability plot, generalized DBSCAN | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN* |
| Srodne | 3 | 3 | 3 |
| Sažetak≠ | OPTICS (Ordering Points To Identify the Clustering Structure) is a density-based clustering algorithm introduced by Ankerst, Breunig, Kriegel, and Sander in 1999. It generalizes DBSCAN by processing points in an ordering that encodes the full density-based cluster structure of a dataset, enabling the detection of clusters of varying densities through a reachability plot rather than requiring a fixed global density threshold. | DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes. | HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions. |
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