Machine learning

Hierarchical Clustering

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.

MethodMind'de açSoonVideoSoon

Tam yöntemi oku

Members only

Sources

Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI: 10.1080/01621459.1963.10500845 ↗

Referenced by

Affinity Propagation Bayesian Cluster Analysis Bayesian Hierarchical Clustering Community Detection DBSCAN DTW Barycenter Averaging Explainable K-Means Formal Concept Analysis Gaussian Mixture Model K-means K-Means Clustering Mean Shift Online K-means OPTICS Principal Component Analysis Robust Hierarchical Clustering Robust k-means Spectral Clustering Stochastic Block Model

Bu sayfada bir hata mı var? Bildir / düzeltme öner →

Tam yöntemi oku

Sources

Related methods

Referenced by