Compara mètodes

	Anàlisi de clústers	Escalament Multidimensional (MDS)
Camp	Estadística	Estadística
Família	Latent structure	Latent structure
Any d'origen	1939–1967	1952–1964
Autor original	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means	Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)
Tipus	Unsupervised classification / grouping	Dimensionality reduction / visualization
Font seminal	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913	Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.
Àlies	clustering, unsupervised classification, data clustering, numerical taxonomy	MDS, metric MDS, non-metric MDS, proximity scaling
Relacionats	5	5
Resum	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.	Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data.