Salīdzināt metodes

	Hierarhiskā klasterizācija	Lineārās diskriminanta analīze (LDA
Nozare	Mašīnmācīšanās	Statistika
Saime	Machine learning	Hypothesis test
Izcelsmes gads	1963	1936
Autors	Ward, J. H.	Ronald A. Fisher
Tips	Unsupervised clustering (agglomerative)	Parametric linear classifier / dimensionality reduction
Pirmavots	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.
Citi nosaukumi	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis
Saistītās	4	7
Kopsavilkums	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.	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.