เปรียบเทียบวิธี

	Ensemble HDBSCAN	Ensemble K-means	การจัดกลุ่มแบบ K-means
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning	Machine learning
ปีกำเนิด	2011–2017	2002	1967 (formalized 1982)
ผู้ริเริ่ม	Vega-Pons, S. & Ruiz-Shulcloper, J. (ensemble clustering framework); McInnes, L. et al. (HDBSCAN base)	Strehl, A. & Ghosh, J.	MacQueen, J. B.; Lloyd, S. P.
ประเภท	Consensus clustering ensemble	Ensemble clustering (consensus aggregation of K-means partitions)	Partitional clustering
แหล่งต้นตำรับ	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.	Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
ชื่อเรียกอื่น	HDBSCAN ensemble clustering, consensus HDBSCAN, multi-run HDBSCAN, cluster ensemble HDBSCAN	consensus K-means, K-means ensemble clustering, cluster ensemble with K-means, EKM	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
ที่เกี่ยวข้อง	4	3	4
สรุป	Ensemble HDBSCAN runs HDBSCAN multiple times under different hyperparameter settings or data subsamples and combines the resulting partitions into a single stable consensus clustering. Because HDBSCAN is sensitive to its minimum cluster size and minimum samples parameters, pooling multiple runs greatly reduces sensitivity to any single configuration and yields more reproducible cluster assignments on noisy, high-dimensional data.	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.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.