เปรียบเทียบวิธี

	DBSCAN	การจัดกลุ่มแบบลำดับชั้น	การวิเคราะห์องค์ประกอบหลัก	UMAP
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning	Machine learning	Machine learning
ปีกำเนิด	1996	1963	2002	2018
ผู้ริเริ่ม	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Ward, J. H.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	McInnes, L.; Healy, J.; Melville, J.
ประเภท	Density-based clustering algorithm	Unsupervised clustering (agglomerative)	Unsupervised dimensionality reduction	Nonlinear manifold-learning dimension reduction
แหล่งต้นตำรับ	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.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426.
ชื่อเรียกอื่น	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	UMAP (Uniform Manifold Approximation and Projection), uniform manifold approximation and projection, manifold dimension reduction
ที่เกี่ยวข้อง	3	4	3	5
สรุป	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.	UMAP (Uniform Manifold Approximation and Projection) is a fast, scalable nonlinear dimension-reduction method grounded in manifold-learning theory, introduced by McInnes, Healy and Melville in 2018. It compresses high-dimensional data into a low-dimensional embedding for visualisation and downstream analysis.