Methoden vergleichen

	Hierarchische Clusteranalyse	Lineare Diskriminanzanalyse (LDA
Fachgebiet	Maschinelles Lernen	Statistik
Familie	Machine learning	Hypothesis test
Entstehungsjahr	1963	1936
Urheber	Ward, J. H.	Ronald A. Fisher
Typ	Unsupervised clustering (agglomerative)	Parametric linear classifier / dimensionality reduction
Wegweisende Quelle	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.
Aliasnamen	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis
Verwandt	4	7
Zusammenfassung	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.