So sánh phương pháp

	K-means Tổ hợp	HDBSCAN
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2002	2013
Người khởi xướng	Strehl, A. & Ghosh, J.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
Loại	Ensemble clustering (consensus aggregation of K-means partitions)	Hierarchical density-based clustering
Công trình gốc	Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.	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.
Tên gọi khác	consensus K-means, K-means ensemble clustering, cluster ensemble with K-means, EKM	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*
Liên quan	3	3
Tóm tắt	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.	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.