Methoden vergleichen

	K-Means-Clustering	Lineare Diskriminanzanalyse (LDA
Fachgebiet	Maschinelles Lernen	Statistik
Familie	Machine learning	Hypothesis test
Entstehungsjahr	1967	1936
Urheber	MacQueen, J.	Ronald A. Fisher
Typ	Partitional clustering (centroid-based)	Parametric linear classifier / dimensionality reduction
Wegweisende Quelle	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.
Aliasnamen	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis
Verwandt	3	7
Zusammenfassung	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.	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.