So sánh phương pháp

	Robust Multiple Correspondence Analysis	Phân tích cụm
Lĩnh vực	Thống kê	Thống kê
Họ	Latent structure	Latent structure
Năm ra đời	2000s	1939–1967
Người khởi xướng	Extensions by Hubert, Rousseeuw and collaborators; building on classical MCA by Benzécri (1973) and Greenacre (1984)	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
Loại	Robust multivariate dimension reduction	Unsupervised classification / grouping
Công trình gốc	Greenacre, M. J. (2017). Correspondence Analysis in Practice (3rd ed.). Chapman & Hall / CRC Press, Boca Raton. ISBN: 978-1498731775	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
Tên gọi khác	Robust MCA, Outlier-resistant MCA, Robust HOMALS	clustering, unsupervised classification, data clustering, numerical taxonomy
Liên quan	4	5
Tóm tắt	Robust Multiple Correspondence Analysis extends classical MCA to datasets containing outlying or atypical rows of categorical data. By downweighting influential observations before the singular value decomposition, it produces a low-dimensional map of category relationships that faithfully represents the bulk of the data rather than being distorted by a handful of anomalous cases.	Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.