قارن الطرق

	تجميع العنقودية باستخدام المتوسطات (K-Means Clustering)	التجميع الهرمي	تحليل التمييز الخطي (LDA
المجال	تعلم الآلة	تعلم الآلة	الإحصاء
العائلة	Machine learning	Machine learning	Hypothesis test
سنة النشأة	1967	1963	1936
صاحب الطريقة	MacQueen, J.	Ward, J. H.	Ronald A. Fisher
النوع	Partitional clustering (centroid-based)	Unsupervised clustering (agglomerative)	Parametric linear classifier / dimensionality reduction
المصدر التأسيسي	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.	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.
الأسماء البديلة	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis
ذات صلة	3	4	7
الملخص	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.	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.