Σύγκριση μεθόδων

	Ανάλυση Πολλαπλής Αντιστοιχίας (Robust MCA)	Ανάλυση Συμπλεγμάτων
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Latent structure	Latent structure
Έτος προέλευσης	2000s	1939–1967
Δημιουργός	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
Τύπος	Robust multivariate dimension reduction	Unsupervised classification / grouping
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	Robust MCA, Outlier-resistant MCA, Robust HOMALS	clustering, unsupervised classification, data clustering, numerical taxonomy
Συναφείς	4	5
Σύνοψη	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.