HDBSCAN
HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) on Campello, Moulavi ja Sanderi 2013. aasta töödes tutvustatud tiheduspõhine klastrite moodustamise algoritm. See laiendab DBSCAN-i, ehitades täieliku tiheduspõhiste klastrite hierarhia kõikidel tihedusskaaladel ning seejärel eraldades stabiilse tasase jaotuse, muutes selle vastupidavaks andmestikele, kus klastrite tihedused erinevad piirkonniti oluliselt.
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Allikad
- 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: 10.1007/978-3-642-37456-2_14 ↗
- Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data, 10(1), Article 5. DOI: 10.1145/2733381 ↗
- McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI: 10.21105/joss.00205 ↗
Kuidas sellele lehele viidata
ScholarGate. (2026, June 3). Hierarchical Density-Based Spatial Clustering of Applications with Noise. ScholarGate. https://scholargate.app/et/machine-learning/hdbscan
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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