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K-Means聚类×层次聚类×线性判别分析 (LDA×
领域机器学习机器学习统计学
方法族Machine learningMachine learningHypothesis test
起源年份196719631936
提出者MacQueen, J.Ward, J. H.Ronald A. Fisher
类型Partitional clustering (centroid-based)Unsupervised clustering (agglomerative)Parametric linear classifier / dimensionality reduction
开创性文献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. link ↗Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. DOI ↗
别名K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clusteringHiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clusteringLDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis
相关347
摘要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.
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ScholarGate方法对比: K-Means Clustering · Hierarchical Clustering · Linear Discriminant Analysis (Classification). 于 2026-06-19 检索自 https://scholargate.app/zh/compare